text:
\section{Introduction}

Graphs, or networks, are a general and flexible data structure to encode complex relationships among objects.
Examples of real-world graphs include social networks, airline networks, protein-protein interaction networks, and traffic networks. Recently, there has been increasing interest from both academic and industrial communities in analyzing graphical data. Examples span a variety of domains and applications such as node classification~\cite{cao2015grarep,tang2015line} and link prediction~\cite{gao2011temporal,wang2011community} in social networks, role prediction in protein-protein interaction networks \cite{krogan2006global}, and prediction of information diffusion in social and citation networks~\cite{newman2004finding}.

One fundamental task of graph analysis is community detection, which aims to cluster nodes into multiple groups called communities. Each community is a set of nodes that are more closely connected to each other than to nodes in different communities. A community level description is able to capture important information about a graph's global structure. Such a description is useful in many real-world applications, such as identifying users with similar interests in social networks \cite{newman2004finding} or proteins with similar functionality in biochemical networks \cite{krogan2006global}. Community detection has been extensively studied in the literature, and a number of methods have been proposed, including algorithmic approaches \cite{ahn2010link,derenyi2005clique} and probabilistic models \cite{gopalan2013efficient,mcauley2014discovering,yang2013overlapping,yang2013community}. A classical approach to detect communities is spectral clustering~\cite{white2005spectral}, which assumes that neighboring nodes tend to belong to the same communities and detects communities by finding the eigenvectors of the graph Laplacian.

Another important task of graph analysis is node representation learning, where nodes are described using low-dimensional features. Node representations effectively capture local graph structure and are often used as features for many prediction tasks. Modern methods for learning node embeddings \cite{grover2016node2vec,perozzi2014deepwalk,tang2015line} have proved effective on a variety of tasks such as node classification \cite{cao2015grarep,tang2015line}, link prediction \cite{gao2011temporal,wang2011community} and graph visualization \cite{tang2016node,wang2016structural}.

Clustering, which captures the global structure of graphs, and learning node embeddings, which captures local structure, are typically studied separately. Clustering is often used for exploratory analysis, while generating node embeddings is often done for predictive analysis. However, these two tasks are very correlated and it may be beneficial to perform both tasks simultaneously. The intuition is that (1) node representations can be used as good features for community detection (e.g., through K-means)~\cite{cavallari2017learning,rozemberczki2018gemsec,tsitsulin2018verse}, and (2) the node community membership can provide good contexts for learning node representations~\cite{wang2017community}. However, how to leverage the relatedness of node clustering and node embedding in a unified framework for joint community detection and node representation learning is under-explored.

In this paper, we propose a novel probabilistic generative model called vGraph{} for joint community detection and node representation learning. vGraph{} assumes that each node $v$ can be represented as a mixture of multiple communities and is described by a multinomial distribution over communities $z$, i.e., $p(z|v)$. Meanwhile, each community $z$ is modeled as a distribution over the nodes $v$, i.e., $p(v|z)$. vGraph{} models the process of generating the neighbors for each node. Given a node $u$, we first draw a community assignment $z$ from $p(z|u)$. This indicates which community the node is going to interact with. Given the community assignment $z$, we generate an edge $(u,v)$ by drawing another node $v$ according to the community distribution $p(v|z)$. Both the distributions $p(z|v)$ and $p(v|z)$ are parameterized by the low-dimensional representations of the nodes and communities. As a result, this approach allows the node representations and the communities to interact in a mutually beneficial way. We also design a very effective algorithm for inference with backpropagation. We use variational inference for maximizing the lower-bound of the data likelihood. The Gumbel-Softmax~\cite{jang2016categorical} trick is leveraged since the community membership variables are discrete. Inspired by existing spectral clustering methods~\cite{dong2012clustering}, we added a smoothness regularization term to the objective function of the variational inference routine to ensure that community membership of neighboring nodes is similar. The whole framework of vGraph{} is very flexible and general. We also show that it can be easily extended to detect hierarchical communities.

In the experiment section, we show results on three tasks: overlapping community detection, non-overlapping community detection, and node classification-- all using various real-world datasets. Our results show that vGraph{} is very competitive with existing state-of-the-art approaches for these tasks. We also present results on hierarchical community detection.

\section{Related Work}

\textbf{Community Detection.}
Many community detection methods are based on matrix factorization techniques. Typically, these methods try to recover the node-community affiliation matrix by performing a low-rank decomposition of the graph adjacency matrix or other related matrices \cite{kuang2012symmetric,li2018community,wang2011community,yang2013overlapping}. These methods are not scalable due to the complexity of matrix factorization, and their performance is restricted by the capacity of the bi-linear models. Many other studies develop generative models for community detection. Their basic idea is to characterize the generation process of graphs and cast community detection as an inference problem \cite{yang2013community,zhang2015incorporating,zhou2015infinite}. However, the computational complexity of these methods is also high due to complicated inference. Compared with these approaches, vGraph{} is more scalable and can be efficiently optimized with backpropagation and Gumbel-Softmax~\cite{jang2016categorical,maddison2016concrete}. Additionally, vGraph{} is able to learn and leverage the node representations for community detection.

\textbf{Node Representation Learning.}
The goal of node representation learning is to learn distributed representations of nodes in graphs so that nodes with similar local connectivity tend to have similar representations. Some representative methods include DeepWalk \cite{perozzi2014deepwalk}, LINE \cite{tang2015line}, node2vec \cite{grover2016node2vec} and GraphRep \cite{cao2015grarep}. Typically, these methods explore the local connectivity of each node by conducting random walks with either breadth-first search~\cite{perozzi2014deepwalk} or depth-first search~\cite{tang2015line}. Despite their effectiveness in a variety of applications, these methods mainly focus on preserving the local structure of graphs, therefore ignoring global community information. In vGraph{}, we address this limitation by treating the community label as a latent variable. This way, the community label can provide additional contextual information which enables the learned node representations to capture the global community information.

\textbf{Framework for node representation learning and community detection.}
There exists previous work~\cite{cavallari2017learning,jia2019communitygan,tsitsulin2018verse,tu2018unified,wang2017community} that attempts to solve community detection and node representation learning jointly. However, their optimization process alternates between community assignment and node representation learning instead of simultaneously solving both tasks \cite{cavallari2017learning,tu2018unified}.  Compared with these methods, vGraph{} is scalable and the optimization is done end-to-end.

\textbf{Mixture Models.}
Methodologically, our method is related to mixture models, particularly topic models (e.g. PSLA \cite{hofmann1999probabilistic} and LDA \cite{blei2003latent}). These methods simulate the generation of words in documents, in which topics are treated as latent variables, whereas we consider generating neighbors for each node in a graph, and the community acts as a latent variable. Compared with these methods, vGraph{} parameterizes the distributions with node and community embeddings, and all the parameters are trained with backpropagation.

\section{Problem Definition}
 Graphs are ubiquitous in the real-world. Two fundamental tasks on graphs are community detection and learning node embeddings, which focus on global and local graph structures respectively and hence are naturally complementary. In this paper, we study jointly solving these two tasks.
Let $\mathcal{G} = (\mathcal{V}, \mathcal{E})$ represent a graph, where $\mathcal{V}=\{v_1,\ldots ,v_V\}$ is a set of vertices and $\mathcal{E}=\{e_{ij}\}$ is the set of edges. Traditional graph embedding aims to learn a node embedding $\boldsymbol{\phi}_i \in \mathbb{R}^d$ for each $v_i \in \mathcal{V}$ where $d$ is predetermined. Community detection aims to extract the community membership $\mathcal{F}$ for each node. Suppose there are $K$ communities on the graph $\mathcal{G}$, we can denote the community assignment of node $v_i$ as $\mathcal{F}(v_i) \subseteq \{1,...,K\}$. We aim to jointly learn node embeddings $\boldsymbol{\phi}$ and community affiliation of vertices $\mathcal{F}$.

\section{Methodology}

\begin{figure}%
    \centering
    \subfloat[vGraph{}]{{\includegraphics[width=6cm]{images/graphical-1.png} }}%
    \qquad
    \subfloat[Hierarchical vGraph{}]{{\includegraphics[width=6.3cm]{images/graphical-2.png} }}%
  \caption{The diagram on the left represents the graphical model of vGraph{} and the diagram on the right represents the graphical model of the hierarchical extension. $\boldsymbol{\phi}_n$ is the embedding of node $w_n$, $\boldsymbol{\psi}$ denotes the embedding of communities, and $\boldsymbol{\varphi}$ denotes the embeddings of nodes used in $p(c|z)$. Refer to Eq.~\ref{eq:softmax1} and Eq.~\ref{eq:softmax2}.}
\label{fig:graphical_model}
\end{figure}

In this section, we introduce our generative approach vGraph{}, which aims at collaboratively learning node representations and detecting node communities. Our approach assumes that each node can belong to multiple communities representing different social contexts \cite{epasto2019single}. Each node should generate different neighbors under different social contexts. vGraph{} parameterizes the node-community distributions by introducing node and community embeddings. In this way, the node representations can benefit from the detection of node communities. Similarly, the detected community assignment can in turn improve the node representations. Inspired by existing spectral clustering methods \cite{dong2012clustering}, we add a smoothness regularization term that encourages linked nodes to be in the same communities.

\subsection{vGraph{}}

vGraph models the generation of node neighbors. It assumes that each node can belong to multiple communities. For each node, different neighbors will be generated depending on the community context. Based on the above intuition, we introduce a prior distribution $p(z|w)$ for each node $w$ and a node distribution $p(c|z)$ for each community $z$. The generative process of each edge $(w,c)$ can be naturally characterized as follows: for node $w$, we first draw a community assignment $z \sim p(z|w)$, representing the social context of $w$ during the generation process. Then, the linked neighbor $c$ is generated based on the assignment $z$ through $c \sim p(c|z)$. Formally, this generation process can be formulated in a probabilistic way:
\begin{equation}

	p(c|w) = \sum_z p(c|z)p(z|w).
	\label{eq:simple}
\end{equation}

vGraph{} parameterizes the distributions $p(z|w)$ and $p(c|z)$ by introducing a set of node embeddings and community embeddings. Note that different sets of node embeddings are used to parametrize the two distributions. Specifically, let $\boldsymbol{\phi}_i$ denote the embedding of node $i$ used in the distribution $p(z|w)$, $\boldsymbol{\varphi}_i$ denote the embedding of node $i$ used in $p(c|z)$, and $\boldsymbol{\psi}_j$ denote the embedding of the $j$-th community. The prior distribution $p_{\boldsymbol{\phi},\boldsymbol{\psi}}(z|w)$ and the node distribution conditioned on a community $p_{\boldsymbol{\psi},\boldsymbol{\varphi}}(c|z)$ are parameterized by two softmax models:

\begin{equation} \label{eq:softmax1}
    p_{\boldsymbol{\phi},\boldsymbol{\psi}}(z=j|w) =\frac{\mathrm{exp}(\boldsymbol{\phi}^{T}_w \boldsymbol{\psi}_j)}{\sum_{i=1}^K\mathrm{exp}(\boldsymbol{\phi}^{T}_w \boldsymbol{\psi}_i)},
\end{equation}

\begin{equation} \label{eq:softmax2}
    p_{\boldsymbol{\psi},\boldsymbol{\varphi}}(c|z=j) =\frac{\mathrm{exp}(\boldsymbol{\psi}_j^{T} \boldsymbol{\varphi}_c )}{\sum_{c^{'} \in \mathcal{V}}\mathrm{exp}(\boldsymbol{\psi}_j^{T} \boldsymbol{\varphi}_{c^{'}})}.
\end{equation}
Calculating Eq.~\ref{eq:softmax2} can be expensive as it requires summation over all vertices. Thus, for large datasets we can employ negative sampling as done in LINE \cite{tang2015line} using the following objective function:
\begin{equation} \label{eq:neg}
\log \sigma(\boldsymbol{\varphi}_c^T\cdot \boldsymbol{\psi}_j)+\sum_{i=1}^KE_{v\sim P_n(v)}[\log \sigma(-\boldsymbol{\varphi}_{v}^T\cdot \boldsymbol{\psi}_j)],
\end{equation}
where $\sigma(x)=1/(1+\exp(-x))$, $P_n(v)$ is a noise distribution, and $K$ is the number of negative samples. This, combined with stochastic optimization, enables our model to be scalable

To learn the parameters of vGraph{}, we try to maximize the log-likelihood of the observed edges, i.e., $\mathrm{log}~p_{\boldsymbol{\phi},\boldsymbol{\varphi},\boldsymbol{\psi}}(c|w)$. Since directly optimizing this objective is intractable for large graphs, we instead optimize the following evidence lower bound (ELBO)~\cite{kingma2013auto}:
\begin{equation}
\label{eq:lowerbound}
\begin{split}

	\mathcal{L}= E_{z\sim q(z|c,w)}[\mathrm{log}~p_{\boldsymbol{\psi},\boldsymbol{\varphi}}(c|z)]-\mathrm{KL}(q(z|c,w)||p_{\boldsymbol{\phi},\boldsymbol{\psi}}(z|w))
\end{split}
\end{equation}
where $q(z|c,w)$ is a variational distribution that approximates the true posterior distribution $p(z|c,w)$, and $\mathrm{KL}(\cdot||\cdot)$ represents the Kullback-Leibler divergence between two distributions.

Specifically, we parametrize the variational distribution $q(z|c,w)$ with a neural network as follows:
\begin{equation} \label{eq:softmax3}
 q_{\boldsymbol{\phi},\boldsymbol{\psi}}(z=j|w,c) =\frac{\mathrm{exp}((\boldsymbol{\phi}_w \odot \boldsymbol{\phi}_c)^{T} \boldsymbol{\psi}_j)}{\sum_{i=1}^K\mathrm{exp}((\boldsymbol{\phi}_w \odot \boldsymbol{\phi}_c)^{T} \boldsymbol{\psi}_i)}.
\end{equation}
where $\odot$ denotes element-wise multiplication. We chose element-wise multiplication because it is symmetric and it forces the representation of the edge to be dependent on both nodes.

The variational distribution $q(z|c,w)$ represents the community membership of the edge $(w,c)$. Based on this, we can easily approximate the community membership distribution of each node $w$, i.e., $p(z|w)$ by aggregating all its neighbors:
\begin{equation}
    p(z|w)= \sum_c p(z,c|w) = \sum_c p(z|w,c)p(c|w) \approx \frac{1}{|N(w)|} \sum_{c \in N(w)} q(z|w,c),
\end{equation}
where $N(w)$ is the set of neighbors of node $w$. To infer non-overlapping communities, we can simply take the $\argmax$ of $p(z|w)$. However, when detecting overlapping communities instead of thresholding $p(z|w)$ as in \cite{jia2019communitygan}, we use
\begin{equation}
    \mathcal{F}(w)= \{ \argmax_k q(z=k|w,c) \}_{c \in N(w)}.
\end{equation}
That is, we assign each edge to one community and then map the edge communities to node communities by gathering nodes incident to all edges within each edge community as in \cite{ahn2010link}.

\noindent \textbf{Complexity.} Here we show the complexity of vGraph. Sampling an edge takes constant time, thus calculating Eq.~\eqref{eq:neg} takes $\mathcal{O}(d(M+1))$ time, where $M$ is the number of negative samples and $d$ is the dimension of embeddings (the node embeddings and community embeddings have the same dimension).
To calculate Eq. \eqref{eq:softmax3}, it takes $\mathcal{O}(dK)$ time where $K$ is the number of communities. Thus, an iteration with one sample takes $\mathcal{O}(\max(dM, dK))$ time. In practice the number of updates required is proportional to the number of edges $\mathcal{O}(|\mathcal{E}|)$, thus the overall time complexity of vGraph is $\mathcal{O}(|\mathcal{E}|d\max(M, K))$.

\subsection{Community-smoothness Regularized Optimization}
For optimization, we need to optimize the lower bound \eqref{eq:lowerbound} w.r.t. the parameters in the variational distribution and the generative parameters. If $z$ is continuous, the reparameterization trick~\cite{kingma2013auto} can be used. However, $z$ is discrete in our case. In principle, we can still estimate the gradient using a score function estimator \cite{glynn1990likelihood,williams1992simple}. However, the score function estimator suffers from a high variance, even when used with a control variate. Thus, we use the Gumbel-Softmax reparametrization~\cite{jang2016categorical,maddison2016concrete} to obtain gradients for the evidence lower bound. More specifically, we use the straight-through Gumbel-Softmax estimator~\cite{jang2016categorical}.

A community can be defined as a group of nodes that are more similar to each other than to those outside the group \cite{pei2015nonnegative}. For a non-attributed graph, two nodes are similar if they are connected and share similar neighbors. However, vGraph{} does not explicitly weight local connectivity in this way. To resolve this, inspired by existing spectral clustering studies~\cite{dong2012clustering}, we augment our training objective with a smoothness regularization term that encourages the learned community distributions of linked nodes to be similar. Formally, the regularization term is given below:
\begin{equation}
    \mathcal{L}_{reg} = \lambda  \sum_{(w,c) \in \mathcal{E}}  \alpha_{w,c} \cdot d(p(z|c), p(z|w))
\end{equation}
where $\lambda$ is a tunable hyperparameter , $\alpha_{w,c}$ is a regularization weight, and $d(\cdot,\cdot)$ is the distance between two distributions (squared difference in our experiments).
Motivated by \cite{rozemberczki2018gemsec}, we set $\alpha_{w,c}$ to be the Jaccard's coefficient of node $w$ and $c$, which is given by:
\begin{equation}
    \alpha_{w,c} = \frac{|N(w) \cap N(c)|}{|N(w) \cup N(c)|},
\end{equation}
where $N(w)$ denotes the set of neighbors of $w$.
The intuition behind this is that $\alpha_{w,c}$ serves as a similarity measure of how similar the neighbors are between two nodes. Jaccard's coefficient is used for this metric and thus the higher the value of Jaccard's coefficient, the more the two nodes are encouraged to have similar distribution over communities.

By combining the evidence lower bound and the smoothness regularization term, the entire loss function we aim to minimize is given below:
\begin{equation}
     \mathcal{L} = -E_{z\sim q_{\boldsymbol{\phi},\boldsymbol{\psi}}(z|c,w)}[\mathrm{log}~p_{\boldsymbol{\psi},\boldsymbol{\varphi}}(c|z)] + \mathrm{KL}(q_{\boldsymbol{\phi},\boldsymbol{\psi}}(z|c,w)||p_{\boldsymbol{\phi},\boldsymbol{\psi}}(z|w)) + \mathcal{L}_{reg}
\end{equation}
For large datasets, negative sampling can be used for the first term.

\subsection{Hierarchical vGraph{}} \label{subsection:hier}

One advantage of vGraph{}'s framework is that it is very general and can be naturally extended to detect hierarchical communities. In this case, suppose we are given a $d$-level tree and each node is associate with a community, the community assignment can be represented as a $d$-dimensional path vector $\vec{z} = (z^{(1)}, z^{(2)}, ..., z^{(d)})$, as shown in Fig.~\ref{fig:graphical_model}. Then, the generation process is formulated as below: (1) a tree path $\vec{z}$ is sampled from a prior distribution $p_{\boldsymbol{\phi},\boldsymbol{\psi}}(\vec{z}|w)$. (2) The context $c$ is decoded from $\vec{z}$ with $p_{\boldsymbol{\psi},\boldsymbol{\varphi}}(c|\vec{z})$. Under this model, the likelihood of the network is
\begin{equation}
	p_{\boldsymbol{\phi},\boldsymbol{\varphi},\boldsymbol{\psi}}(c|w) = \sum_{\vec{z}} p_{\boldsymbol{\phi},\boldsymbol{\psi}}(c|\vec{z})p_{\boldsymbol{\phi},\boldsymbol{\psi}}(\vec{z}|w).
\end{equation}
At every node of the tree, there is an embedding vector associated with the community. Such a method is similar to the hierarchical softmax parameterization used in language models \cite{morin2005hierarchical}.

\begin{comment}
\subsection{Optimization}
By adding the evidence lower bound and the smoothness regularization term, the entire loss function we aim to minimize is given below:
\begin{equation}
     \mathcal{L} = -E_{z\sim q_{\boldsymbol{\phi},\boldsymbol{\psi}}(z|c,w)}[\mathrm{log}~p_{\boldsymbol{\psi},\boldsymbol{\varphi}}(c|z)] + \mathrm{KL}(q_{\boldsymbol{\phi},\boldsymbol{\psi}}(z|c,w)||p_{\boldsymbol{\phi},\boldsymbol{\psi}}(z|w)) + \mathcal{L}_{reg}
\end{equation}
It is straightforward to obtain the gradient of the last two terms. For the first term, we can estimate the gradient using a score function estimator \cite{glynn1990likelihood,williams1992simple}. However, the score function estimator suffers from high variance, even when used with a control variate. Thus instead of exact sampling, we use Gumbel-Softmax reparametrization~\citep{jang2016categorical,maddison2016concrete}.

Given an edge $(w, c)$, we denote the output class probabilities of the $K$-categorical distribution $q_{\boldsymbol{\phi},\boldsymbol{\psi}}(z|c,w)$ by $[e_1, e_2, ..., e_K]$. Instead of sampling $z \sim \mathrm{Discrete}([e_1, e_2, ..., e_K])$, we sample a continuous relaxation $\widetilde{\bb{z}}$ from a density $Q_{\boldsymbol{\phi},\boldsymbol{\psi}}(z|c,w)$ where the output class probabilities $[y_1, y_2, ..., y_K]$ is defined as:
\begin{equation}
\small
y_k =\frac{\mathrm{exp}((\mathrm{log}(e_k)+g_k)/\tau)}{\sum_{i=1}^K\mathrm{exp}((\mathrm{log}(e_i)+g_i)/\tau)} \; \text{with} \; g_i=-\mathrm{log}(-\mathrm{log}(u_i)), u_i \sim U(0,1)
\end{equation}
where $U$ denotes an uniform distribution
and $\tau \in (0,1]$ is the softmax temperature. That is, we can denote $\widetilde{\bb{z}} \sim Q_{\boldsymbol{\phi},\boldsymbol{\psi}}(z|c,w)$ using a differentiable transformation $f_{\boldsymbol{\phi},\boldsymbol{\psi}}(c,w,g)$ of an (auxiliary) noise variable $\vec{g} = [g_1, g_2, ..., g_K]$:
\begin{equation}
    \widetilde{\bb{z}} = f_{\boldsymbol{\phi},\boldsymbol{\psi}}(c,w,\vec{g})  \; \text{with} \; g_i=-\mathrm{log}(-\mathrm{log}(u_i)), u_i \sim U(0,1)
\end{equation}

The relaxed objective is:
\meng{In the following formula, the proposal distribution still depends on the parameter $\boldsymbol{\phi},\boldsymbol{\psi}$. We can follow Eq.(5) of Auto-Encoding Variational Bayes to revise the formula.}
\begin{align} \label{eq:relax}
    E_{z\sim q_{\boldsymbol{\phi},\boldsymbol{\psi}}(z|c,w)}[\mathrm{log}~p_{\boldsymbol{\psi},\boldsymbol{\varphi}}(c|z)] & \overset{\text{relax}}{\leadsto}  E_{\widetilde{\bb{z}}\sim Q_{\boldsymbol{\phi},\boldsymbol{\psi}}(z|c,w)}[\mathrm{log}~p_{\boldsymbol{\psi},\boldsymbol{\varphi}}(c|\widetilde{\bb{z}})] \\
     & = \sum_{(w,c) \in \mathcal{E}} \mathrm{log}~h_{\boldsymbol{\psi},\boldsymbol{\varphi}}(f_{\boldsymbol{\phi},\boldsymbol{\psi}}(c,w,\vec{g}))
\end{align}
where $h_{\boldsymbol{\psi},\boldsymbol{\varphi}}$ the function of $p_{\boldsymbol{\psi},\boldsymbol{\varphi}}(c|\widetilde{\bb{z}})$.
The gradient of the relaxed objective can be directly calculated by backpropagation. In all experiments, we use straight-through Gumbel-Softmax. For the forward pass, we use
\begin{equation}
\widetilde{\bb{z}}_{\text{forward}} = \text{one\_hot} (\argmax_i [y_i] ).
\end{equation}
However, the continuous approximation (Eq. \ref{eq:relax}) is used during the backward pass.
\meng{I don't understand why we can use one-hot vector in forward pass and use continuous vector in backward pass.}
\end{comment}

\section{Experiments}
As vGraph{} can detect both overlapping and non-overlapping communities, we evaluate it on three tasks: overlapping community detection, non-overlapping community detection, and vertex classification.

\subsection{Datasets}

We evaluate vGraph{} on 20 standard graph datasets.
For non-overlapping community detection and node classification, we use 6 datasets: Citeseer, Cora, Cornell, Texas, Washington, and Wisconsin. For overlapping communtiy detection, we use 14 datasets, including Facebook, Youtube, Amazon, Dblp, Coauthor-CS. For Youtube, Amazon, and Dblp, we consider subgraphs with the 5 largest ground-truth communities due to the runtime of baseline methods. To demonstrate the scalability of our method, we additionally include visualization results on a large dataset -- Dblp-full. Dataset statistics are provided in Table~\ref{tab:dataset}. More details about the datasets is provided in Appendix A.

\subsection{Evaluation Metric}

For overlapping community detection, we use \textit{F1-Score} and \textit{Jaccard Similarity} to measure the performance of the detected communities as in \cite{yang2013community,li2018community}. For non-overlapping community detection, we use \textit{Normalized Mutual Information (NMI)} \cite{tian2014learning} and  \textit{Modularity}. Note that Modularity does not utilize ground truth data. For node classification, \textit{Micro-F1} and \textit{Macro-F1} are used
\begin{comment}
\begin{equation}
\small
\begin{aligned}
 \frac{1}{2}(\frac{1}{|C^*|}\sum_{C^*_i \in \mathcal{C}^*}\max_{C_j \in \mathcal{C}} \delta(C^*_i, C_j) + \frac{1}{|C|}\sum_{C_i \in \mathcal{C}}\max_{C^*_j \in \mathcal{C}^*} \delta(C^*_j, C_i)). \nonumber
\end{aligned}
\end{equation}
Where $\delta(C^*_i, C_j)$ is defined as the harmonic mean (F1-score) of $C^*_i$ and $C_j$. The Jaccard Similarity score is defined by $\delta(C^*_i, C_j) = \frac{|C^*_i \cap C_j|}{|C^*_i \cup C_j|}$. For both metrics, larger values indicate better performance.
\end{comment}

\subsection{Comparative Methods}
For overlapping community detection, we choose four competitive baselines: \textbf{BigCLAM} \cite{yang2013overlapping}, a nonnegative matrix factorization approach based on the Bernoulli-Poisson link that only considers the graph structure; \textbf{CESNA} \cite{yang2013community}, an extension of BigCLAM, that additionally models the generative process for node attributes; \textbf{Circles} \cite{mcauley2014discovering}, a generative model of edges w.r.t. attribute similarity to detect communities; and \textbf{SVI} \cite{gopalan2013efficient}, a Bayesian model for graphs with overlapping communities that uses a mixed‐membership stochastic blockmodel.

To evaluate node embedding and non-overlapping community detection, we compare our method with the five baselines: \textbf{MF} \cite{wang2011community}, which represents each vertex with a low-dimensional vector obtained through factoring the adjacency matrix; \textbf{DeepWalk} \cite{perozzi2014deepwalk}, a method that adopts truncated random walk and Skip-Gram to learn vertex embeddings; \textbf{LINE} \cite{tang2015line}, which aims to preserve the first-order and second-order proximity among vertices in the graph; \textbf{Node2vec} \cite{grover2016node2vec}, which adopts biased random walk and Skip-Gram to learn vertex embeddings; and \textbf{ComE} \cite{cavallari2017learning}, which uses a Gaussian mixture model to learn an embedding and clustering jointly using random walk features.

\subsection{Experiment Configuration}

For all baseline methods, we use the implementations provided by their authors and use the default parameters. For methods that only output representations of vertices, we apply K-means to the learned embeddings to get non-overlapping communities. Results report are averaged over 5 runs. \textbf{No node attributes} are used in all our experiments. We generate node attributes using node degree features for those methods that require node attributes such as CESNA \cite{yang2013community} and Circles \cite{mcauley2014discovering}. It is hard to compare the quality of community results when the numbers of communities are different for different methods. Therefore, we set the number of communities to be detected, $K$, as the number of ground-truth communities for all methods, as in \cite{li2018community}. For vGraph{}, we use full-batch training when the dataset is small enough. Otherwise, we use stochastic training with a batch size of 5000 or 10000 edges. The initial learning rate is set to 0.05 and is decayed by 0.99 after every 100 iterations. We use the Adam optimizer and we trained for 5000 iterations. When smoothness regularization is used, $\lambda$ is set to $100$. For community detection, the model with the lowest loss is chosen. For node classification, we evaluate node embeddings after 1000 iterations of training. The dimension of node embeddings is set to 128 in all experiments for all methods. For the node classification task, we randomly select 70\% of the labels for training and use the rest for testing.

\begin{table}[!t]
\caption{Evaluation (in terms of F1-Score and Jaccard Similarity) on networks with overlapping ground-truth communities. NA means the task is not completed in 24 hours. In order to evaluate the effectiveness of smoothness regularization, we show the result of our model with (vGraph{}+) and without the regularization.}
\centering
\scalebox{.68}{
\begin{tabular}{|c|c|c|c|c|c|c||c|c|c|c|c|c|}
\hline
\multicolumn{7}{|c||}{F1-score}                                                                                & \multicolumn{6}{c|}{Jaccard}                                                             \\ \hline
Dataset      & Bigclam         & CESNA           & Circles         & SVI    & vGraph          & vGraph+         & Bigclam         & CESNA           & Circles & SVI    & vGraph          & vGraph+         \\ \hline
facebook0    & \textbf{0.2948} & 0.2806          & 0.2860          & 0.2810 & 0.2440          & 0.2606          & \textbf{0.1846} & 0.1725          & 0.1862  & 0.1760 & 0.1458          & 0.1594          \\
facebook107  & \textbf{0.3928} & 0.3733          & 0.2467          & 0.2689 & 0.2817          & 0.3178          & \textbf{0.2752} & 0.2695          & 0.1547  & 0.1719 & 0.1827          & 0.2170          \\
facebook1684 & 0.5041          & \textbf{0.5121} & 0.2894          & 0.3591 & 0.4232          & 0.4379          & 0.3801          & \textbf{0.3871} & 0.1871  & 0.2467 & 0.2917          & 0.3272          \\
facebook1912 & 0.3493          & 0.3474          & 0.2617          & 0.2804 & 0.2579          & \textbf{0.3750} & 0.2412          & 0.2394          & 0.1672  & 0.2010 & 0.1855          & \textbf{0.2796} \\
facebook3437 & 0.1986          & 0.2009          & 0.1009          & 0.1544 & 0.2087          & \textbf{0.2267} & 0.1148          & 0.1165          & 0.0545  & 0.0902 & 0.1201          & \textbf{0.1328} \\
facebook348  & 0.4964          & 0.5375          & 0.5175          & 0.4607 & \textbf{0.5539} & 0.5314          & 0.3586          & 0.4001          & 0.3927  & 0.3360 & \textbf{0.4099} & 0.4050          \\
facebook3980 & 0.3274          & 0.3574          & 0.3203          & NA     & \textbf{0.4450} & 0.4150          & 0.2426          & 0.2645          & 0.2097  & NA     & \textbf{0.3376} & 0.2933          \\
facebook414  & 0.5886          & 0.6007          & 0.4843          & 0.3893 & 0.6471          & \textbf{0.6693} & 0.4713          & 0.4732          & 0.3418  & 0.2931 & 0.5184          & \textbf{0.5587} \\
facebook686  & 0.3825          & 0.3900          & 0.5036          & 0.4639 & 0.4775          & \textbf{0.5379} & 0.2504          & 0.2534          & 0.3615  & 0.3394 & 0.3272          & \textbf{0.3856} \\
facebook698  & 0.5423          & 0.5865          & 0.3515          & 0.4031 & 0.5396          & \textbf{0.5950}  & 0.4192          & 0.4588          & 0.2255  & 0.3002 & 0.4356          & \textbf{0.4771} \\
Youtube      & 0.4370          & 0.3840          & 0.3600          & 0.4140 & 0.5070          & \textbf{0.5220} & 0.2929          & 0.2416          & 0.2207  & 0.2867 & 0.3434          & \textbf{0.3480} \\
Amazon       & 0.4640          & 0.4680          & \textbf{0.5330} & 0.4730 & \textbf{0.5330} & 0.5320          & 0.3505          & 0.3502          & 0.3671  & 0.3643 & 0.3689          & \textbf{0.3693} \\
Dblp         & 0.2360          & 0.3590          & NA              & NA     & 0.3930          & \textbf{0.3990} & 0.1384          & 0.2226          & NA      & NA     & 0.2501          & \textbf{0.2505} \\
Coauthor-CS  & 0.3830          & 0.4200          & NA              & 0.4070 & 0.4980          & \textbf{0.5020} & 0.2409          & 0.2682          & NA      & 0.2972 & \textbf{0.3517} & 0.3432          \\ \hline
\end{tabular}
}
\label{tab:overlapping}
\end{table}

\subsection{Results}

Table \ref{tab:overlapping} shows the results on overlapping community detection. Some of the methods are not very scalable and cannot obtain results in 24 hours on some larger datasets. Compared with these studies, vGraph{} outperforms all baseline methods in 11 out of 14 datasets in terms of F1-score or Jaccard Similarity, as it is able to leverage useful representations at node level. Moreover, vGraph{} is also very efficient on these datasets, since we use employ variational inference and parameterize the model with node and community embeddings. By adding the smoothness regularization term (vGraph{}+), we see a farther increase performance, which shows that our method can be combined with concepts from traditional community detection methods.

The results for non-overlapping community detection are presented in Table~\ref{tab:nonoverlapping}. vGraph{} outperforms all conventional node embeddings + K-Means in 4 out of 6 datasets in terms of NMI and outperforms all 6 in terms of modularity. ComE, another framework that jointly solves node embedding and community detection, also generally performs better than other node embedding methods + K-Means. This supports our claim that learning these two tasks collaboratively instead of sequentially can further enhance performance. Compare to ComE, vGraph{} performs better in 4 out of 6 datasets in terms of NMI and 5 out of 6 datasets in terms of modularity. This shows that vGraph{} can also outperform frameworks that learn node representations and communities together

Table~\ref{tab:node} shows the result for the node classification task. vGraph{} significantly outperforms all the baseline methods in 9 out of 12 datasets. The reason is that most baseline methods only consider the local graph information without modeling the global semantics. vGraph{} solves this problem by representing node embeddings as a mixture of communities to incorporate global context.

\begin{table}[]
\caption{Evaluation (in terms of NMI and Modularity) on networks with non-overlapping ground-truth communities.}
\centering
\scalebox{.68}{

\begin{tabular}{|c|c|c|c|c|c|c||c|c|c|c|c|c|}
\hline
\multicolumn{7}{|c||}{NMI}                                                                       & \multicolumn{6}{|c|}{Modularity}                                           \\ \hline
Dataset    & MF     & deepwalk & LINE            & node2vec & ComE            & vGraph{}              & MF     & deepwalk & LINE   & node2vec & ComE            & vGraph{}              \\ \hline
cornell    & 0.0632 & 0.0789   & 0.0697          & 0.0712   & 0.0732          & \textbf{0.0803} & 0.4220 & 0.4055   & 0.2372 & 0.4573   & 0.5748          & \textbf{0.5792} \\
texas      & 0.0562 & 0.0684   & \textbf{0.1289} & 0.0655   & 0.0772          & 0.0809         & 0.2835 & 0.3443   & 0.1921 & 0.3926   & \textbf{0.4856} & 0.4636          \\
washington & 0.0599 & 0.0752   & \textbf{0.0910} & 0.0538   & 0.0504          & 0.0649          & 0.3679 & 0.1841   & 0.1655 & 0.4311   & 0.4862          & \textbf{0.5169} \\
wisconsin  & 0.0530 & 0.0759   & 0.0680          & 0.0749   & 0.0689          & \textbf{0.0852} & 0.3892 & 0.3384   & 0.1651 & 0.5338   & 0.5500          & \textbf{0.5706} \\
cora       & 0.2673 & 0.3387   & 0.2202          & 0.3157   & \textbf{0.3660} & 0.3445          & 0.6711 & 0.6398   & 0.4832 & 0.5392   & 0.7010          & \textbf{0.7358} \\
citeseer   & 0.0552 & 0.1190   & 0.0340          & 0.1592   & \textbf{0.2499} & 0.1030          & 0.6963 & 0.6819   & 0.4014 & 0.4657   & 0.7324          & \textbf{0.7711} \\ \hline
\end{tabular}
}
\label{tab:nonoverlapping}
\end{table}

\begin{table}
\caption{Results of node classification on 6 datasets.}
\centering
\scalebox{.68}{
\begin{tabular}{|c|c|c|c|c|c|c||c|c|c|c|c|c|}
\hline
\multicolumn{7}{|c||}{Macro-F1}                                            & \multicolumn{6}{c|}{Micro-F1}                                                     \\ \hline
Datasets   & MF    & DeepWalk & LINE  & Node2Vec & ComE  & vGraph         & MF    & DeepWalk       & LINE  & Node2Vec       & ComE           & vGraph         \\ \hline
Cornell    & 13.05 & 22.69    & 21.78 & 20.70    & 19.86 & \textbf{29.76} & 15.25 & 33.05          & 23.73 & 24.58          & 25.42          & \textbf{37.29} \\
Texas      & 8.74  & 21.32    & 16.33 & 14.95    & 15.46 & \textbf{26.00} & 14.03 & 40.35          & 27.19 & 25.44          & 33.33          & \textbf{47.37} \\
Washington & 15.88 & 18.45    & 13.99 & 21.23    & 15.80 & \textbf{30.36} & 15.94 & 34.06          & 25.36 & 28.99          & 33.33          & \textbf{34.78} \\
Wisconsin  & 14.77 & 23.44    & 19.06 & 18.47    & 14.63 & \textbf{29.91} & 18.75 & \textbf{38.75} & 28.12 & 25.00          & 32.50          & 35.00          \\
Cora       & 11.29 & 13.21    & 11.86 & 10.52    & 12.88 & \textbf{16.23} & 12.79 & 22.32          & 14.59 & 27.74          & \textbf{28.04} & 24.35          \\
Citeseer   & 14.59 & 16.17    & 15.99 & 16.68    & 12.88 & \textbf{17.88} & 15.79 & 19.01          & 16.80 & \textbf{20.82} & 19.42          & 20.42          \\ \hline
\end{tabular}
}
\label{tab:node}
\end{table}

\subsection{Visualization}

In order to gain more insight, we present visualizations of the facebook107 dataset in Fig.~\ref{fig:vis}(a). To demonstrate that our model can be applied to large networks, we present results of vGraph{} on a co-authorship network with around 100,000 nodes and 330,000 edges in Fig.~\ref{fig:vis}(b).
More visualizations are available in appendix B. We can observe that the community structure, or ``social context'', is reflected in the corresponding node embedding (node positions in both visualizations are determined by t-SNE of the node embeddings).
To demonstrate the hierarchical extension of our model, we visualize a subset of the co-authorship dataset in Fig.~\ref{fig:hier-vis}. We visualize the first-tier communities and second-tier communities in panel (a) and (b) respectively. We can observe that the second-tier communities grouped under the same first-tier communities interact more with themselves than they do with other second-tier communities

\begin{figure}[]
    \centering
    \subfloat[]{{\includegraphics[width=5cm]{images/facebook107.png} }}%
    \qquad
    \subfloat[]{{\includegraphics[width=4cm]{images/dblp5000-new.png} }}%
    \caption{In panel (a) we visualize the result on the facebook107 dataset using vGraph{}. In panel (b) we visualize the result on Dblp-full dataset using vGraph{}. The coordinates of the nodes are determined by t-SNE of the node embeddings.}%
    \label{fig:vis}
\end{figure}

\begin{figure}[]
    \centering
    \subfloat[]{{\includegraphics[width=3.7cm]{images/dblp-1-new.png} }}%
    \qquad
    \subfloat[]{{\includegraphics[width=3.5cm]{images/dblp-2-new.png} }}%
     \qquad
    \subfloat[]{{\includegraphics[width=3.5cm]{images/tree.png} }}%
    \caption{We visualize the result on a subset of Dblp dataset using two-level hierarchical vGraph{}. The coordinates of the nodes are determined by t-SNE of the node embeddings. In panel (a) we visualize the first-tier communities. In panel (b), we visualize the second-tier communities. In panel (c) we show the corresponding hierarchical tree structure.}%
    \label{fig:hier-vis}
    \vspace{-4mm}

\end{figure}

\section{Submission of papers to NeurIPS 2019}

\input{1-introduction}
\input{2-related-work}

\input{3-model}
\input{4-experiment}

\section{Conclusion}
In this paper, we proposed vGraph{}, a method that performs overlapping (and non-overlapping) community detection and learns node and community embeddings at the same time. vGraph{} casts the generation of edges in a graph as an inference problem. To encourage collaborations between community detection and node representation learning, we assume that each node can be represented by a mixture of communities, and each community is defined as a multinomial distribution over nodes. We also design a smoothness regularizer in the latent space to encourage neighboring nodes to be similar.
Empirical evaluation on 20 different benchmark datasets demonstrates the effectiveness of the proposed method on both tasks compared to competitive baselines. Furthermore, our model is also readily extendable to detect hierarchical communities.

\medskip

\bibliographystyle{plain}

\section{Datasets}
Citeseer, Cora, Cornell, Texas, Washington, and Wisconsin are available online\footnote{https://linqs.soe.ucsc.edu}. For Youtube, Amazon, and Dblp, we consider subgraphs with the 5 largest ground-truth communities due to the runtime of the baseline methods.

\textbf{Facebook}\footnote{https://snap.stanfod.edu/data/ego-Facebook.html} is a set of Facebook ego-networks. It contains 10 different ego-networks with identified circles. Social circles formed by friends are regarded as ground-truth communities.

\textbf{Youtube}\footnote{http://snap.stanford.edu/data/com-Youtube.html} is a network of social relationships of Youtube users. The vertices represent users; the edges indicate friendships among the users; the user-defined groups are considered as ground-truth communities

\textbf{Amazon}\footnote{http://snap.stanford.edu/data/com-Amazon.html} is collected by crawling amazon website. The vertices represent products and the edges indicate products frequently purchased together. The ground-truth communities are defined by the product categories on Amazon

\textbf{Dblp}\footnote{http://snap.stanford.edu/data/com-DBLP.html} is a co-authorship network from Dblp. The vertices represent researchers and the edges indicate co-author relationships. Authors who have published in a same journal or conference form a community

\textbf{Coauthor-CS}\footnote{https://aminer.org/aminernetwork} is a computer science co-authorship network. We chose 21 conferences and group them into five categories: \textit{Machine Learning}, \textit{Computer Linguistics}, \textit{Programming language}, \textit{Data mining}, and \textit{Database}

\newpage
\section{Visualization}

\begin{figure}[!h]
\includegraphics[width=.76\linewidth]{images/facebook1684.png}
\caption{Visualization of the result of vGraph on the facebook1684 dataset. The coordinates of the nodes are determined by t-SNE of the node embeddings.}
\end{figure}

\begin{figure}[]
 \includegraphics[width=.76\linewidth]{images/facebook107.png}
\caption{Visualization of the result of vGraph on the facebook107 dataset. The coordinates of the nodes are determined by t-SNE of the node embeddings.}
\end{figure}

\begin{figure}[]
   \includegraphics[width=.8\linewidth]{images/facebook414.png}
   \caption{Visualization of the result of vGraph on the facebook414 dataset. The coordinates of the nodes are determined by t-SNE of the node embeddings.}
\end{figure}

\begin{figure}[]
   \includegraphics[width=.8\linewidth]{images/youtube5.png}
   \caption{Visualization of the result of vGraph on the Youtube dataset. The coordinates of the nodes are determined by t-SNE of the node embeddings.}
\end{figure}


To live independently in a flat - warden assisted or with friends.
To work with horses, be a groom, work on an event yard. Be a professional rider and help people with their horses.
Manage her own money - banking, budgeting and shopping.
Take care of herself and her belongings including hygiene and diet.
Take responsibility for completion of tasks.
Plan and carry out journeys safely using public transport.
For more photographs and information about this student click below.


Nope, no one said they were missing my raps. But that's okay. A rap about Candice Cameron and her Hallmark movies just came to me, so obv I'm gonna post it here for you.
I was having writer's block and Matt was making suggestions about what I could write about this week. Should I write about how I prep for a plane trip with three kids? Nah, I don't really have anything to say about it anymore. Maybe after three I've just learned to relax a little, enjoy some wine and trust that they'll (mostly) behave themselves on the plane. Honestly, I'd give them all like a 9.9/10 as travelers anyway, minus Shep being a crappy travel-sleeper, but third time around, I know he'll outgrow it soon.
What were Matt's other suggestions. Oh. How I felt about our trip back. I think I covered that already though. A holiday gift guide? I mean I could…but I actually went back and read the one I did last year and was like woah, I can actually use this for my shopping this year, brahaha.
So when he suggested something tongue and cheek about my love for holiday movies, I knew it was Hallmark-movie-rap-gold when it just fell out of the sky into my head. He was like "wait…you just wrote that right now?" Yep. That's how Jay-Z, Cardi-B, and like, I don't know — Weird Al Yankovic do it. I feel like Kimmy Schmidt here, but me too, Titus. Me. Too.
Obviously a rap about Candice Cameron who is in like every single Hallmark Movie, so much so that Matt's all you have to call this: "Catch Cameron in these Christmas Classics".
PS - this rap is based on a true story hehe. I went to a Friends of the Family Gala and both Melissa Joan Hart and Candace Cameron were there, and well, I guess you're just going to have to read my rap. Word to your mother.
Did I say hi? Did they in return respond hello?
Will she be a Shoe Addict, or a Real Estate Queen?
Here's the Christmas Movie Lineup!
Thank you for the list, MP!
Check us out on Instagram, Pinterest and Youtube!


text:
GuestPosts
Fight Picks
Category: UFC
by David King	 May 24, 2019
Time for MMA to expand the heavyweight division?
What MMA promotions should do in order to prevent size from determining the outcomes of fights.
UFC 128 fight predictions
Predictions for the UFC 128 MMA fight event, scheduled for March 19, 2011.
UFC Live 3: Diego Sanchez vs. Martin Kampmann predictions
Predictions for the third UFC on versus MMA fight event, featuring Diego Sanchez versus Martin Kampmann.
News special
Strikeforce Challenger 14 results
BJ Penn confirms plans to return to the lightweight division
UFC 160 video blog
Full view here
UFC 127 weigh-ins
Exchange between Michael Bisping and Jorge Rivera starts at 5:40.
UFC Fight Night 18 VBlog: Dana White goes off on Loretta Hunt at the end
UFC on FX 8: All about the Brazilians
UFC on FX 8 turned out to be another decent fight card even though there were only a few well-known mixed martial arts (MMA) fighters on the fight card. At the …
UFC on FX 8 turned out to be another decent fight card even though there were only a few well-known mixed martial arts (MMA) fighters on the fight card. At the end of the day, Vitor Belfort proved he's still a complete badass, Jacare made a strong statement in his UFC debut, while Brazilian MMA fans didn't seem to notice the lack of big-name mixed martial artists on the fight card.
Here's a link to my recap posted on Yahoo! for those of you who missed what turned out to be an entertaining fight card: http://sports.yahoo.com/news/ufc-fx-8-results-vitor-belfort-shines-again-025600216.html
The biggest winner at UFC on FX was clearly Belfort, as the Brazilian MMA fighter established himself as one of the top middleweights around. Here's my piece on why Vitor is clearly the best middleweight in the UFC excluding Anderson Silva: http://sports.yahoo.com/news/vitor-belfort-legitimate-number-one-contender-ufcs-middleweight-191400494.html
What are your thoughts on UFC on FX 8?
How Mark Hunt's fans secured the bout against Junior dos Santos
Just as former Strikeforce champion Alistair Overeem pulled out of his UFC 160 battle with former heavyweight kingpin Junior Dos Santos – Australian crowd-pleaser Mark Hunt's loyal fanbase hit social media …
Just as former Strikeforce champion Alistair Overeem pulled out of his UFC 160 battle with former heavyweight kingpin Junior Dos Santos - Australian crowd-pleaser Mark Hunt's loyal fanbase hit social media in hopes of hyping him into the fight. Stunningly, it worked!
UFC President Dana White cracked the whip on this one - ordering the bout in place of the originally planned heavyweight clash after the big fan swamping.
In fact, it took just one ring of the phone to get Hunt to sign the contract. The "Super Samoan" has waited forever for his career takeoff opportunity. The Aussie born mixed martial arts (MMA) fighter said repeatedly about "never turning down a fight" throughout his thrilling career.
The Main Event of UFC 160 at the MGM Grand in Las Vegas, Nevada is Cain Velasquez against Antonio "Big Foot" Silva - the winner of the Dos Santos-Hunt bout, which plays as the co-feature, perhaps may be next for the winner of that fight. This card plays a massive role in the UFC Heavyweight division.
Hunt is coming off a staggering destruction of Dutch skyscraper Stefan Struve. He demolished the tall man moments into Round 3. Nobody, even his worst critics can deny that another win here against Dos Santos won't warrant him a top seat in the UFC title picture.
Dos Santos is a tall task though. Although he likes to scrap, Dos Santos would be the biggest danger to Hunt if he wraps him up with his Jiu Jitsu skills and tweaks his brutal boxing skillset to a more defensive manner.
Knowing what Dos Santos desires - expect a toe-to-toe war. It's an impulsive style that two regional celebrities will do anything to get through. You will see a fight. You will see true showmanship. It's not a fight to overlooked.
Whatever does happen, be a victory for Hunt or Dos Santos, perhaps it would be best to get the fans behind them in a slugfest. The UFC doesn't work with fighters who bring "stinkers", it's about who impresses and who sells themselves as the ultimate world class MMA fighter.
Original article by David King
Rewritten by Corey Quincy
Benson Henderson plans to be more entertaining
UFC Lightweight champion Benson Henderson may be the best scrap metal pulled from the now shredded World Extreme Cagefighting (WEC). The man going by the nickname "Smooth" has technically dissembled ever …
UFC Lightweight champion Benson Henderson may be the best scrap metal pulled from the now shredded World Extreme Cagefighting (WEC). The man going by the nickname "Smooth" has technically dissembled ever opponent he has faced so far - gripping and defending the WFC's world championship in barely a year.
While taking two close decision victories over fan-favorite Frankie Edgar, he didn't turn too many heads - half of the mixed martial arts (MMA) populace thought he should have been defeated.
Henderson is aware he needs to make a statement, he needs to make his opponents crumble, fans cheer. He needs to market, to promote. That is the outside-the-ring job for an MMA fighter.
Henderson recently expressed his admiration for Chael Sonnen's marketing ability. Sonnen's loud mouth and unsettled hype earned him an undeserved beating by Jon Jones just a week ago. Henderson doesn't attract this national television, mainstream hype that Sonnen does. That is the blockade between Henderson and superstar.
Henderson scored a hard fought victory over Nate Diaz at UFC on Fox 5 in December - Diaz was left on the ground for a pummeling, it's the easy way to avoid his recognized striking skills. Henderson then battled to a controversial split-decision over Gilbert Melendez - the luck continues.
While marketing is vital for flowing on the world level stage, The bottom line is Henderson is an intelligent mixed martial arts fighter with the ability to shed off win-to-win. He doesn't talk smack, he doesn't score brutal knockouts - he wins fights!
There is a popular consensus that you must rave fans to remain an elite MMA fighter. I guide you to Georges St-Pierre. GSP prefers a lay n' prey type technique, but he racks up the wins.
Henderson has the same base. Keep the wins coming, the fans will follow.
Is Cung Le the most underrated middleweight in the UFC?
While lacking substantial marketability in the UFC, Cung Le is one of the greatest rising MMA fighters the promotion has to offer. He has been the betting underdog in two of …
While lacking substantial marketability in the UFC, Cung Le is one of the greatest rising MMA fighters the promotion has to offer.
He has been the betting underdog in two of his last three fights - he pulled off victories in two. In his most notable bout he halted the comeback of Rich Franklin with a career traumatizing first round KO.
Concluding a career in Kickboxing, Cung Le decided to test his worth in the enticing sport of mixed martial arts (MMA). He went 6-0 in Strikeforce prior to a stunning loss to Scott Smith - which he quickly avenged two fights and two more victories later before retreating to the UFC.
While lacking the materialistic benefits for the UFC, Cung hasn't failed to entertain and impress the hardcore mixed martial arts community. Distracted from a high-profile acting career, Le unfortunately lost his UFC debut against Wanderlei Silva due to a series of hellacious knees in round 2. He has since returned more focused than ever with eye-opening victories to Franklin and Patrick Cote.
If his cardio held up a bit better during his UFC debut against "The Axe Murderer", "The Man with the Iron fist" would likely be 3-0 in the promotion.
His fight with Cote was a demonstration of his improvement and focus, careful punch-placing allowed him to showcase his world class striking skills over three rounds to win a 30-27 decision.
He also impressed the blood n' guts side of the sport with one right hand that knocked out former world middleweight champion Rich Franklin less than two minutes into their Fuel TV televised collision. It was a perfectly timed low-kick on the aging warrior that set up one of the best knockouts of last year.
Albeit inexperienced in the sport of MMA, Le has phenomenal striking skills and underrated grappling skills. He can give any UFC fighter a run for his money.
You can grant credit to Le's striking skills to his undermined background in Sanshou, where he spent many years developing his unorthodox killer-KO kickboxing ability. He was the IKF (International Kickboxing Federation), but he also has shown few dents in his grappling skills.
Given his decent takedown defense, some of MMA's top wrestlers will struggle to topple the Vietnamese warrior.
With the Franklin scrapping, Le should be able to challenge his worth in that form - against a top ten UFC Middleweight.
GSP might be willing to face Anderson Silva under the right circumstances
Georges St. Pierre, UFC welterweight champion was dignified as is typical during a recent episode of "UFC Connected," where he kindly acknowledged Anderson Silva, UFC's middleweight champion as the all-time greatest …
Georges St. Pierre, UFC welterweight champion was dignified as is typical during a recent episode of "UFC Connected," where he kindly acknowledged Anderson Silva, UFC's middleweight champion as the all-time greatest competitor in UFC history.
Georges seemed to be more open to the idea of a matchup between himself and Silva, the Brazilian mixed martial arts (MMA) legend, so long as he's afforded the time to bulk up for it.
It's difficult to blame GSP for his reasoning, considering that Anderson was previously a contender in the light-heavyweight division. Anderson had little trouble, weighing in at 205 lb. he KO'd James Irvin in his first light-heavyweight match. This continued in his even more impressive second fight, easily KO'ing the former UFC light-heavyweight champion Forrest Griffin.
Silva maintains a healthy advantage over those facing him, making it foolish for GSP to allow him the size advantage as well. Despite even the best preparation on GSP's part he'll still find himself at a disadvantage against Silva.
Though the majority of MMA fans are pulling for this fight to happen, it's unlikely that it will, given that it would favor Silva and would be unfair to GSP. Besides GSP may at one point exceed Silva's record of nine title defenses in the UFC and I doubt if Dana White is willing to give up the Canadian 170-pound chanp.
Original article written by David King
Rewritten by Chauncey Kent
Ronda Rousey on transgender MMA Fighter Fallon Fox
From the very beginning Fallon Fox has caused a considerable stir in the mixed martial arts (MMA) community. There's something that just seems unfair about a transgender competitor attempting to compete …
From the very beginning Fallon Fox has caused a considerable stir in the mixed martial arts (MMA) community. There's something that just seems unfair about a transgender competitor attempting to compete with females born into the gender.
Ronda Rousey, UFC bantamweight champion maintained some strong opinions of the subject of Fox's eligibility to fight vs. biologically born women that she expressed in a New York Post interview not long ago.
"She can try hormones, chop her pecker off, but it's still the same bone structure a man has," expressed Rousey. "It's an advantage. I don't think it's fair… It's not something that happened to her. It was a decision she made. She should be aware in her career after that; it's going to be an arduous path. I don't know why she's surprised by that. It's going to draw a lot of emotions."
Rousey isn't alone in her opinion with several other female UFC contenders feeling likewise, such as Miesha Tate who revealed her disapproval some time ago. Rousey wisely avoided the mistakes of fellow UFC fighter Matt Mitrione, who expressed his similar opinion in an unnecessarily insulting and disrespectful way earning him an indefinite suspension and as such needn't worry about the UFC's management coming down on her for respectfully expressing her concerns.
The question isn't so much should Fallon Fox be allowed to compete in MMA as it is, should she be allowed to compete against biologically born women when she was born a male. While some medical experts assert her biology is very similar to a biologically born female's as a result of the hormone therapy, there are those that respectfully disagree. The issue boils down to the relatively recent nature of gender reassignment. There hasn't been much research done on its effects which complicates the issue.
Is it possible that political correctness is causing some of the problems? Definitely! No one would argue against the fact that the MMA press deserves a good deal of credit for its stance against discrimination, but sometimes this can be taken too far. A good deal of those expressing their concerns about Fox are rapidly considered "misinformed," and disregarded despite the fact that placing a transgender in a cage with a biologically born female without knowing the playing field is truly even is equally ignorant.
UFC's Dana White lashes out at Bob Arum
Dana White, President of the UFC will talk openly about any topic and as a result he had some fascinating insights to share regarding Manny Pacquiao's knockout defeat against Juan Manuel …
Dana White, President of the UFC will talk openly about any topic and as a result he had some fascinating insights to share regarding Manny Pacquiao's knockout defeat against Juan Manuel Marquez during their December 8th matchup.
Dana's opinion of the subject is that Bob Arum, the CEO of Top Rank Promotions made a bad move scheduling Pacquiao for a fourth matchup with Marquez. The logic behind this is that there were easier options available for the Filipino congressman.
As far as the business aspect is concerned White's point is a valid one, taking into account Pacquiao didn't do all that well in his most recent two fights before December 8th, securing a controversial split-decision in his match against Marquez and then running into some bad luck in his match with Timothy Bradley on June 9th, somehow finding himself on the losing end of a decision most fans felt he should've won.
Pacquiao clearly wasn't doing as well as he had been a few years ago and so setting up a match with a person most people in the boxing community believed had bested him in their first few fights was unwise.
At this point Pacquiao has been defeated consecutively for the first time in his career meaning he no longer has much chance of arranging a super-fight with Floyd Mayweather at least without being willing to ask "how high?" each time Floyd says "jump" in upcoming negotiations.
The choice by Arum to pit Pacquiao against a competitor who has already proven himself more than a match for him will be criticized for some time, especially taking into account that Bradley was strongly pushing for a rematch.
Bradley and Manny's pay-per-view fight in June was hardly record setting with only 950,000 buyers; however it's not a stretch to conclude that a rematch between the two would have sparked more interest. When the Judges handed the welterweight title over to Bradley, they made him the most hated individual in the Philippines, even more so than Mayweather. Because of this, a rematch would have brought the Filipino fan-base out to watch their champion take back what's rightfully his.
While on paper, the fourth matchup of Marquez and Pacquiao seems the most profitable move, the actual pay-per-view purchases barely peaked 1 million where as a rematch with Bradley would have done just as well if not better.
Don't think this means I'll be letting White off the hook. White has always been critical of Arum and promoters like him for not setting up the best fights possible. Because of this it's a bit hypocritical to blame "Uncle Bob" for going with the more challenging fight. Dana frequently asserts that the mission of the UFC is to provide mixed martial arts (MMA) fans with the best fights they can arrange, despite the fact that he obviously is in favor of protecting the big earners.
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text:
Q: MySQL save results of EXECUTE in a variable? How do I save the results of EXECUTE statement to a variable? Something like
SET a = (EXECUTE stmtl);

A: If you want to do this with a prepared statement, then you need to include the variable assignment in the original statement declaration.
If you want to use a stored routine it's easier. You can assign the return value of a stored function directly to a variable, and stored procedures support out parameters.
Examples:
Prepared Statement:
PREPARE square_stmt from 'select pow(?,2) into @outvar';
set @invar = 1;
execute square_stmt using @invar;
select @outvar;
+---------+
| @outvar |
+---------+
|       1 |
+---------+
DEALLOCATE PREPARE square_stmt;

Stored Function:
delimiter $$
create function square_func(p_input int) returns int
begin
  return pow(p_input,2);
end $$
delimiter ;

set @outvar = square_func(2);
select @outvar;
+---------+
| @outvar |
+---------+
|       4 |
+---------+

Stored Procedure:
delimiter $$
create procedure square_proc(p_input int, p_output int)
begin
  set p_output = pow(p_input,2);
end $$
delimiter ;

set @outvar = square_func(3);
call square_proc(2,@outvar);
select @outvar;
+---------+
| @outvar |
+---------+
|       9 |
+---------+

redpajama_set_name: RedPajamaStackExchange


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A recent Wired magazine includes an article on "crowdsourcing". This is an Internet phenomenon: the Internet provides access to many, many "amateur" practitioners of a skill, such that they now compete with professionals (and lower the prices available to professionals). The first example is stock photography: companies used to pay hundreds or thousands for small numbers of stock photos, but now there are sources of good digital photos available for stock use at $1 a photo. Why pay a hundred times as much when the "amateur" photos are excellent quality?
I believe that a form of "crowdsourcing" has happened to the role playing game industry. Quite apart from the glut of professional products, there are many, many products published by both standard publishers and PDF publishers that are written "by the crowd". Or to put it another way, there are so many fanboys and fangirls willing to write RPG material for nothing or next to nothing, material easy and cheap to find on the Internet, that traditional publishers cannot charge much for their publications, and cannot pay their authors much.
Given the amount paid to most writers (as low as two cents a word, and rarely as much as five cents a word), and the lack of royalty payments (RPG books are "works for hire", you're paid a lump sum and that's it), it's impossible for most writers to get a reasonable return on their time. This is particularly true if they test their material in actual gameplay, so usually they don't. This has contributed to the low quality of professional publications. At the same time there are people who love RPGs, who actual play their new stuff, then write it up and contribute it to the world at large. Some of this is likely to be better than much of the quickly-written junk published by the traditional publishers.
The result of all this: why buy RPG material, or why buy expensive RPG material when the PDFs are likely to be about as good and are much cheaper? And so the bottom has fallen out of the RPG market, with no prospect that it will ever return--because crowdsourcing is here to stay.
It is less likely that this will happen to boardgames, because much of the popularity of boardgames come from the tangible feel of the pieces. You can't put tangible pieces in a PDF, or self-publish them through lulu or xlibris. Though a day might come when there are companies that can mass produce small numbers of tangible pieces to be included in boardgames. Nonetheless there are places like wargamedownloads.com that offer PDF wargames cheaply.
Moreover, without much playtesting hardly any game will be worth a hoot. You can write fairly decent RPG material without testing it, but you can't write a good boardgame without testing it.
Late last year I started a thread on BGG about fundamental structures of games. The brief version of my conclusions is listed below. Now I'm going to try to go through each of the nine elements and list many of the alternatives that are available to designers for that element. I did Economy in late December, now I'll try some more.
7. Conflict resolution/interaction of game entities.
Adjacent conflict (wargames) often with dice or cards, sometimes with "combat tables"
I've made a "current" list of techniques for making Brit-like games more like Euro or German-style games. Can anyone think of something that might be added?
Almost all of these methods are used in "Brit Lite" and have been playtested in that game or another.
I use cards for combat, each player playing one (usually); each player has an identical pile of cards, and has a hand of several of those cards at any given time.
However, I've found by testing that reducing stacking to one in difficult terrain and two in clear just doesn't work right. 2 and 3 seem to be the minimums. But with fewer areas you can end up with fewer pieces. Also, the "overstack" can be one greater than the normal max, perhaps +1 more if a leader is present.


text:
HideMyAss! Review
VPN Review: HideMyAss!
What Is HideMyAss?ReviewPros and ConsInstallation and FeaturesServer NetworkPerformanceSecurity and PrivacyCustomer SupportPricingConclusionCustomer RatingsAlternatives
"I don't have anything to hide, why should I care about online privacy?" Data privacy activists are confronted with this argument, and others like it, on a nearly daily basis. The somewhat apocryphal 'Hawthorne effect' contends that everyone behaves differently, even when they simply think that they are being observed, while abuses of privacy should never be trivialized. Even if you have "nothing to hide".
HideMyAss flips the skeptics' argument, asserting that it is normal to want to hide things and that it is the best service for doing so. With its crass name and cartoonish logo, the provider certainly draws quite a bit of attention to itself; our test shows whether this is justified, or just a load of horse sh...
What Is HideMyAss?
HideMyAss (HMA), founded in 2005, is a UK-based VPN service. Since 2016, HMA has been owned by Avast, a Czech security software developer perhaps best known for their anti-virus programs. HMA meshes well with the rest of Avast's portfolio: Through its huge server network, users can protect their privacy and establish secure connections.
Server Network
Installation and Features
Excellent speeds
Massive server infrastructure, especially in regions typically ignored by other providers
Random IP selection feature
Apps look a bit stale
No split tunneling in the desktop client
Not really well-suited for streaming
After purchasing an HMA license, you can connect the desktop client to your account in one of two ways: Either through logging in with your user data, or entering the activation code that you received following purchase of the license. You only need to log in once, as thereafter, you will always be automatically logged in.
The service's applications are compatible with Windows, macOS, Android, iOS, and Linux, as well as routers, gaming consoles, and smart TVs. Depending on your subscription, you'll be able to register and simultaneously use either 5 or 10 devices.
You can download the installation files for all supported operating systems directly from HMA's website.
The most recent version of HMA has a slightly more tidy interface. Whereas in the past, you could alternate between three different connection modes, now, there's only a single connection area, in which a VPN tunnel is created by clicking on the On/Off button. Below that, you can select your server's location, with "Lightning Fast Connection" set as default.
By default, you'll automatically be connected with the quickest available server, however, you can change this to a specific server.
Once a VPN connection has been created, the donkey changes into a different outfit. Below this, you'll also see your original IP address as well as the new IP address you're using. You can change this IP address by clicking on it, which comes in handy if you're confronted with download limits. In this way, you won't have to disconnect/reconnect to reset your IP address.
We hope that the service offers better protection than its mascot's disguises do.
You can open the server list by clicking on "Location". After that, you'll be able to sort these by region, suitability for streaming or P2P, or to mark favorites by clicking on the heart symbol next to a particular server. Should a country have multiple servers, you can select from these by clicking on the arrow to the right. Here, you'll also be shown if it's a real server or a virtual one.
Unfortunately, despite all of the detail, the server lists don't provide any information about their usage, which could result in poor upload and download speeds, or high pings.
Lots of categories are provided for filtering servers.
By clicking on the "More" tab in the upper right, a submenu will open in which you can find shortcuts to connection settings, the kill switch, data traffic statistics, and a speed test. The last of these allows you to select multiple servers and compare their download speed and ping to one another. After that, you can directly connect to one of the servers.
The speed test helps to find the faster server.
Settings can be accessed through the gear menu in the top left corner of the interface. Our favorite feature is random IP selection, which allows you to automatically change your IP address for a period of time, making tracking even more difficult. This is a handy function for added security that, unfortunately, isn't all that common among the other VPNs from our sample.
Apart from this, you can also look forward to all of the standard settings, such as auto-start and a kill switch. The latter has two modes: The classic version severs your connection as soon as the VPN encounters an issue, whereas the second mode allows you to choose which apps or programs should disconnect from the VPN in the event of a problem. Selecting this mode also creates a VPN connection whenever you open these apps, even if you haven't started HMA.
Changing VPN protocols isn't possible in the apps as these come automatically configured. You'll either have to do this manually outside of the app or download an older version of the desktop client. The desktop client also lacks split tunneling.
With HMA you can schedule automatic IP address changes.
In comparison to the sleeker clients offered by other VPN providers, HMA's efforts seem a bit stale, and on a 4K screen, washed-out. When it comes to looks, there are more visually appealing VPN clients out there.
The smartphone app for HMA is extremely familiar to its desktop client, most likely because the user interfaces are identical. Here too, you'll find the "Lightning Connect" features, as well as streaming servers and favorites. The only notable absence is the P2P category. Unfortunately, favorites you select in the desktop client aren't synchronized with the mobile app.
HMA's mascot also greets you on your smartphone.
Settings are also a bit sparser in the mobile app than in the desktop client, however, split tunneling is offered. This allows you to designate which apps use your VPN connection and which go through your regular connection.
HMA's user-friendly apps aren't particularly impressive, but they don't have any major deficits either, particularly with regards to features. Split tunnel routing, more protocol options, and real-time server data (apart from the speed test) would make it easier to select servers. Overall though, HMA still offers a good package.
HMA's features aren't breathtaking, however, its server infrastructure is. HMA stands way above its competitors, offering more than 1,080 VPN servers, in 280 locations, spread across 190 countries, easily surpassing those who claim to offer "wide-reaching" networks. HMA has servers in almost every corner of the globe, even though some of these are 'virtual servers' (and noted as such).
HideMyAss promises "the biggest VPN network in the world", and comes through!
HMA has even staked its claim in regions where other VPN providers have traditionally been less active, such as Central and South America, as well as Africa, and Asia.
Numerically speaking, in terms of servers, HMA lags behind its competitors, however, its geographic 'spread', and truly global coverage, more than compensate for this. In our mind, the geographic distribution of HMA's server network is second to none.
The speed of all VPNs which we tested was evaluated using a server in Europe with a 1 GB/s connection. We tested random HMA servers in the US and Germany at different times of the day to more accurately assess their performances. More information concerning our methodology can be found in our VPN speed test.
The table below shows the average results from the last 365 days, arranged in order of download and upload speed (descending):
When we performed our assessment, HMA clocked a download speed of 167.7 MB/s and an upload speed of 16.1 MB/s, securing a very respectable fifth place in our ranking. This was not far off from the best providers.
The diagram below shows HMA's average speeds over the past months.
Of course, there's a lot more to performance than just speed, and HMA started out decently here. While using the service for everyday needs, we experienced stable speeds, reliable connections, and no disruptions, such as more captchas than normal.
When we tested HMA for video streaming using its dedicated servers, we had problems accessing Netflix and Amazon Prime Video content from abroad. At the same time, we were able to watch foreign content on Disney+ and the BBC's iPlayer.
Heading to China? You might not want to take the eccentric donkey with you since we couldn't establish any connections during testing. Better options for this include NordVPN, ExpressVPN, or Windscribe.
Nearly all VPN providers promise not to create any logs of their users or their browsing behavior. In most cases, the providers simply have to be taken at their word, leading to scandal when this (far too often) turns out to have not been the case. In the past, HMA couldn't break its "no logs" promise, since it didn't make one to begin with.
Happily, this changed since we last evaluated the service. As of 2021, HMA describes itself as a "No-Log VPN", owing to its 2020 passing of an independent audit conducted by VerSprite.
Of course, being based in the UK remains problematic as that country has been charged by the European Court of Human Rights with engaging in illegal mass surveillance, and is part of the so-called Five Eyes intelligence-sharing agreement. In the worst case, HMA would be legally bound to submit saved information about users and their Internet behavior to British authorities, who, in turn, could share these findings with other governments party to the Five Eyes agreement. Since HMA became a "No-Logs" provider, this has become less of an issue.
When it comes to user security, we didn't find any weaknesses, as the provider passed our VPN leak tests (IPv6, DNS, and WebRTC) without issue. Connections are encrypted with AES 256, the industry-standard, while HMA relies on OpenVPN for its protocols. Unfortunately, WireGuard, the cutting edge in protocols, is not supported.
HMA passed all of EXPERTE.com's leak tests.
Some features, like random IP or the app kill switch also buttress the provider's security.
Although purists might scoff at HMA's earlier stance towards logs, its completion of an independent audit and accompanying "No-Logs" promise should be taken for what they are: big steps in the right direction. Major concerns should be dispelled, leaving users to feel safe entrusting their privacy to the provider.
Since VPN issues often need to be resolved quickly, live chat support (optimally 24/7) is a must for every VPN provider. HMA offers a live chat from 10 AM to 8 PM (UTC).
For less pressing matters, the standard contact form/support ticket is also available.
During testing, we were put in touch with a support staffer within around a minute of entering the live chat.
In the event that you prefer to resolve technical issues on your own, the informative support center offers guides, FAQs, and a forum.
Generally speaking, we felt well taken care of through the live chat, with all of our questions competently answered within a relatively short time. The only downside was that this service isn't available 24/7.
How much you pay for HMA depends on two factors: The length of your subscription (1, 12, or 24 months) and how many devices you would like to use simultaneously (5 or 10). Even though HMA is not one of the most affordable providers we looked at, you can still get a good deal with the 24-month plan.
Set-up fee $0.00 $0.00 $0.00
Base price per month $2.99 $4.99 $10.99
Contract period (months) 24 12 1
Data volume unlimited unlimited unlimited
Number of Devices 5 5 5
Number of servers 1,100 1,100 1,100
Number of countries 210 210 210
No server logs
P2P allowed
Tor access
HMA subscriptions can be paid for by credit card or PayPal. Payment by BitCoin is not possible.
If uncertain, you can test HMA for for a week, but will need to input your payment information before starting your trial. The provider also offers a 30-day, money-back guarantee.
HideMyAss impresses in a number of ways: Its server infrastructure is unparalleled and covers the entire globe, showing that the glaring absence of other VPN providers from certain regions is not something that needs to, or should be, accepted. Some features which HMA offers, like the automatic IP address changer, set an example that the rest of the industry should follow.
In terms of quality, HMA also does a good job, being one of the fastest providers we looked at. This wasn't much different two years ago when we last reviewed the service, but since then, its major shortcoming, the lack of a "No-Logs" promise, has been rectified in the best way imaginable, namely, an independent security audit.
Time has not been so kind to HMA's app though, as it now looks a bit stale compared to those offered by its competitors. We're also still waiting for split tunneling in the desktop version. Compounding this, you won't want to take the donkey on your next trip to China or ask it for help in streaming some content from abroad. In all of these areas, more attractive offers are available.
HideMyAss! VPN
1100+ servers
290 locations in 210 countries
Unreliable for streaming
Data volume
Contract Period
HideMyAss! 36 month
HMA received satisfactory to good ratings in the reviews which we examined. Its price is the most often criticized aspect of its service, whereby, the majority of users seem to be very satisfied with HMA's performance.
We analyzed the reviews and ratings from various portals and found 78,455 ratings with an overall rating of 4.2 out of 5 .
If you're looking for a more modern app, we were impressed with ProtonVPN, even though their server network can't touch HMA's. On the plus side though, ProtonVPN offers a free plan without data limits.
Should you be more interested in a better-performing VPN, HMA came in behind our first and second fastest providers, NordVPN and Surfshark. Both of these also shined in terms of performance.
5400+ servers, 60 countries
No logs
6 devices simultaneously
Inexpensive 2-year package
NordVPN combines speed, user-friendliness and functionality into a coherent overall package that is also fairly affordable.
NordVPN 2-year plan
3000+ Server
160 cities in 94 countries
Saves no logs
ExpressVPN impresses with strong performance, a huge server network and excellently designed applications, but this quality also comes at a higher price.
ExpressVPN 12 Months
3200+ servers in 65 countries
Unlimited number of devices
Many apps (Linux, FireTV, ...)
Very cheap on the 24-month plan
Surfshark is one of the cheapest providers on the market, but besides the price also impresses with strong protection, many features and a great support with 24/7 live chat.
Surfshark 24 months
Strong focus on security
Headquarters in Switzerland
Solid performance
Open Source & external audits
ProtonVPN Free
hide.me VPN
75+ locations in 47+ countries
with free plan
hide.me Free
Italiano Deutsch
hide.me
CyberGhost
IPVanish VPN
Mullvad VPN
VPN Unlimited
PrivateVPN
Avast SecureLine
Perfect Privacy
Avira Phantom
... for Chrome
... for Firefox
... for Germany
... for USA
... for Italy
... for China
... for France
... for Prime Video
... for Netflix
... for Disney+
ExpressVPN vs. NordVPN
VPN Setup
... on Windows
... on macOS
... on iOS
... on Android
... on Raspberry Pi
WireGuard


text:
Home News Marc Cohn Co-Writes Three Tracks on New Album From Blind Boys of...
Marc Cohn Co-Writes Three Tracks on New Album From Blind Boys of Alabama 'Almost Home'
The songwriting skills of GRAMMY Award-winning singer-songwriter Marc Cohn can be heard on the three opening tracks of newly released album Almost Home by legendary gospel group Blind Boys of Alabama's. This album is earning critical praise from NPR Music, No Depression, Jambase and more and is being considered for a GRAMMY for Album Of The Year. Cohn, alongside co-writer, guitarist, and producer John Leventhal, penned the tracks "Stay on the Gospel Side," which they co-wrote with surviving founder Clarence Fountain, "Let My Mother Live" which includes writing contributions from surviving founder Jimmy Carter, and "God Knows Everything." "Let My Mother Live" is being considered for a GRAMMY for Best American Roots Performance and Song.
Cohn and Leventhal have a 25-year-long history working together. They recently co-wrote five songs for soul legend William Bell's GRAMMY Award-winning Americana album of the year, This Is Where I Live.
"After touring with the Blind Boys of Alabama last year, an idea was hatched to write songs based on their own personal journeys. The Blind Boys themselves were the inspiration for the songs we wrote together. It was different from any project I've been involved in, and an honor to collaborate with these legends of Gospel music and to help them tell their stories of loss, music, family and faith. I'm looking forward to performing these songs live with them after the first of the year," said Cohn.
Cohn, best known for his Grammy Award-winning soulful ballad "Walking in Memphis," solidified his place as one of this generation's most compelling singer/songwriters, combining the precision of a brilliant tunesmith with the passion of a great soul man.
He is on tour this fall both solo and with Michael McDonald in support of his albums Careful What you Dream: Lost Songs and Rarities and the bonus album Evolution of a Record, featuring never-before-heard songs and demos dating back to years before his debut album and the GRAMMY Award that followed.
MARC COHN'S US TOUR 2017/2018
OCT 24 – DPAC – Durham, NC *
OCT 26 – Lynn Auditorium – Lynn, MA *
OCT 27 – Merriam Theatre – Philadelphia, PA *
OCT 28 – Warner Theater – Washington, DC *
OCT 30 – Ryman Auditorium – Nashville, TN *
OCT 31 – SPACE – Evanston, IL *
NOV 2 – State Theatre – Minneapolis, MN *
NOV 3 – Peabody Opera House – St Louis, MO *
NOV 4 – Orpheum Theatre – Memphis, TN *
NOV 6 – ACL Live at the Moody Theater – Austin, TX *
NOV 14 – Eccles Theater – Salt Lake City, UT*
NOV 16 – Fox Theater – Oakland, CA *
NOV 17 – Orpheum Theatre – Los Angeles, CA *
JAN 18 – Green Music Center – Rohnert Park, CA
JAN 19 – Bankhead Theater – Livermore, CA
JAN 20 – Cascade Theatre – Redding, CA
JAN 21 – Tower Theatre – Bend, OR
JAN 23 – Lincoln Theatre – Mount Vernon, WA
JAN 25 – Numberica Performing Arts Center – Wenatchee, WA
JAN 26 – Kirkland Performance Center – Kirkland, WA
JAN 27 – Broadway Center for Performing Arts – Tacoma, WA
JAN 31 – Esplanade Arts and Heritage Center – Medicine Hat, Canada
FEB 1 – Jack Singer Concert Hall – Calgary, Canada
FEB 2 – Festival Place – Sherwood Park, Canada
FEB 22 – Sheridan Opera House – Telluride, CO
MAR 14 – The Tin Pan – Richmond, VA
MAR 16 – Mayo Performing Arts Center – Morristown, NJ
MAR 17 – Scottish Rite Auditorium – Collingswood, NJ
*with Michael McDonald
For more information, please visit www.MarcCohn.com
Marc Cohn
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CHICAGO (WLS) -- ABC 7 put you in the driver's seat with a spectacular preview of the 2018 CHICAGO AUTO SHOW on Saturday. ABC 7 PRESENTS THE 2018 CHICAGO AUTO SHOW, hosted by Mark Giangreco and Jim Rose, was a live, hour-long special showcasing all of the bells and whistles that come with some of the finest cars, trucks, and SUVs the industry has to offer. ABC 7's Cheryl Scott, Roz Varon, Val Warner, Ryan Chiaverini, Ji Suk Yi and Ravi Baichwal also contributed. An encore broadcast of the live special will air on the same day at Midnight and again on Sunday, February 18 from 4-5 PM on ABC 7.
Auto Show Chairman John Hennessy takes Mark Giangreco, Tracy Butler and ABC 7 for a whirl around the showroom.
During the special, Auto Show Chairman John Hennessy takes Mark Giangreco, Tracy Butler and ABC 7 for a whirl around the showroom to spotlight the stand-out offerings of the 2018 show and reveal some surprise attractions. Automakers give the inside scoop on the most exciting vehicles making their debut this year.
Automakers give the inside scoop on the most exciting vehicles making their debut this year.
Traffic anchor Roz Varon shows viewers the best way to get to the nation's largest auto show and even how to save money.
The newest vehicles from Nissan, Volkswagen and Kia are on display at the auto show.
What are the latest tech advancements in the automotive world? ABC 7's Windy City LIVE, Ji Suk Yi hops behind the wheel to showcase the newest technology that takes consumers one step closer to autonomous cars. For example, the Cadillac CT6's long-awaited Super Cruise allows for a true hands-off driving experience, and in case of an emergency, even offers life saving features.
The latest tech advancements in the automotive world and the coolest concept cars.
Weekend Anchor Ravi Baichwal walks down memory Lane with ABC 7 personalities and veteran diehard auto show fans who relive some of their favorite auto show memories from past years.
A look at the latest from Jeep and Ford, plus past auto shows.
Butler also takes you inside the so-called 'Greenest Volvo dealership in the World' to see what great lengths Field's Volvo went through to make a splash on the green scene.
The newest models from Kia, Hyundai, Infiniti and Friday night's First Look for Charity event.
The 2018 Chicago Auto Show special returns for the 4th year to ABC 7 through a partnership with the Chicago Automobile Trade Association and ABC 7 Chicago - pairing the most watched television station in Chicago with the largest auto show in North America. This year will mark the 110th edition of the show.
New offerings from Fiat and Cherolet at this year's auto show.
Chicago new-car dealers have been outstanding community partners, lending support to local schools, Little League teams and local charities for years. The 2018 Auto Show profiles dealerships like Wickstrom Auto Group and the Bredemann Family of Dealerships and their amazing commitment to three area charities. The special also highlights the dozens of other dealers who fire up their grills and raise thousands for U.S. troops each summer with their USO Barbecue for the Troops.
This year's auto show has over 1,000 vehicles on display from 40 automakers.
The Auto Show special will be broadcast live on ABC 7's 7.1 and 7.2, available on the ABC 7 News App and will also be available live and on-demand on ABC7Chicago.com.