You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Summarizing semantic graphs: a survey 
Chapter Title:  Mining-based graph summarization 
1. Abstract of Cited Paper (BIB001):  Recent scientific and technological advances have witnessed an abundance of structural patterns modeled as graphs. As a result, it is of special interest to process graph containment queries effectively on large graph databases. Given a graph database G, and a query raph q, the graph containment query is to retrieve all graphs in G which contain q as subgraph(s). Due to the vast number of graphs in G and the nature of complexity for subgraph isomorphism testing, it is desirable to make use of high-quality graph indexing mechanisms to reduce the overall query processing cost. In this paper, we propose a new cost-effective graph indexing method based on frequent tree-features of the graph database. We analyze the effectiveness and efficiency of tree as indexing feature from three critical aspects: feature size, feature selection cost, and pruning power. In order to achieve better pruning ability than existing graph-based indexing methods, we select, in addition to frequent tree-features (Tree), a small number of discriminative graphs (Δ) on demand, without a costly graph mining process beforehand. Our study verifies that (Tree+Δ) is a better choice than graph for indexing purpose, denoted (Tree+Δ ≥Graph), to address the graph containment query problem. It has two implications: (1) the index construction by (Tree+Δ) is efficient, and (2) the graph containment query processing by (Tree+Δ) is efficient. Our experimental studies demonstrate that (Tree+Δ) has a compact index structure, achieves an order of magnitude better performance in index construction, and most importantly, outperforms up-to-date graph-based indexing methods: gIndex and C-Tree, in graph containment query processing. 
2. Abstract of Cited Paper (BIB002):  OLAP (On-Line Analytical Processing) is an important notion in data analysis. Recently, more and more graph or networked data sources come into being. There exists a similar need to deploy graph analysis from different perspectives and with multiple granularities. However, traditional OLAP technology cannot handle such demands because it does not consider the links among individual data tuples. In this paper, we develop a novel graph OLAP framework, which presents a multi-dimensional and multi-level view over graphs. The contributions of this work are two-fold. First, starting from basic definitions, i.e., what are dimensions and measures in the graph OLAP scenario, we develop a conceptual framework for data cubes on graphs. We also look into different semantics of OLAP operations, and classify the framework into two major subcases: informational OLAP and topological OLAP. Then, with more emphasis on informational OLAP (topological OLAP will be covered in a future study due to the lack of space), we show how a graph cube can be materialized by calculating a special kind of measure called aggregated graph and how to implement it efficiently. This includes both full materialization and partial materialization where constraints are enforced to obtain an iceberg cube. We can see that the aggregated graphs, which depend on the graph properties of underlying networks, are much harder to compute than their traditional OLAP counterparts, due to the increased structural complexity of data. Empirical studies show insightful results on real datasets and demonstrate the efficiency of our proposed optimizations. 
3. Abstract of Cited Paper (BIB003):  Graphs are prevalent in many domains such as Bioinformatics, social networks, Web and cyber-security. Graph pattern mining has become an important tool in the management and analysis of complexly structured data, where example applications include indexing, clustering and classification. Existing graph mining algorithms have achieved great success by exploiting various properties in the pattern space. Unfortunately, due to the fundamental role subgraph isomorphism plays in these methods, they may all enter into a pitfall when the cost to enumerate a huge set of isomorphic embeddings blows up, especially in large graphs. ::: ::: The solution we propose for this problem resorts to reduction on the data space. For each graph, we build a summary of it and mine this shrunk graph instead. Compared to other data reduction techniques that either reduce the number of transactions or compress between transactions, this new framework, called Summarize-Mine, suggests a third path by compressing within transactions. Summarize-Mine is effective in cutting down the size of graphs, thus decreasing the embedding enumeration cost. However, compression might lose patterns at the same time. We address this issue by generating randomized summaries and repeating the process for multiple rounds, where the main idea is that true patterns are unlikely to miss from all rounds. We provide strict probabilistic guarantees on pattern loss likelihood. Experiments on real malware trace data show that Summarize-Mine is very efficient, which can find interesting malware fingerprints that were not revealed previously. 
4. Abstract of Cited Paper (BIB004):  Traditionally Semantic Web applications either included a web crawler or relied on external services to gain access to the Web of Data. Recent efforts, have enabled applications to query the entire Semantic Web for up-to-date results. Such approaches are based on either centralized indexing of semantically annotated metadata or link traversal and URI dereferencing as in the case of Linked Open Data. They pose a number of limiting assumptions, thus breaking the openness principle of the Web. In this demo we present a novel technique called Avalanche, designed to allow a data surfer to query the Semantic Web transparently. The technique makes no prior assumptions about data distribution. Specifically, Avalanche can perform "live" queries over the Web of Data. First, it gets on-line statistical information about the data distribution, as well as bandwidth availability. Then, it plans and executes the query in a distributed manner trying to quickly provide first answers. 
5. Abstract of Cited Paper (BIB005):  Graph has been a ubiquitous and essential data representation to model real world objects and their relationships. Today, large amounts of graph data have been generated by various applications. Graph summarization techniques are crucial in uncovering useful insights about the patterns hidden in the underlying data. However, all existing works in graph summarization are single-process solutions, and as a result cannot scale to large graphs. In this paper, we introduce three distributed graph summarization algorithms to address this problem. Experimental results show that the proposed algorithms can produce good quality summaries and scale well with increasing data sizes. To the best of our knowledge, this is the first work to study distributed graph summarization methods. 
6. Abstract of Cited Paper (BIB006):  How can we succinctly describe a million-node graph with a few simple sentences? Given a large graph, how can we find its most "important" structures, so that we can summarize it and easily visualize it? How can we measure the "importance" of a set of discovered subgraphs in a large graph? Starting with the observation that real graphs often consist of stars, bipartite cores, cliques, and chains, our main idea is to find the most succinct description of a graph in these "vocabulary" terms. To this end, we first mine candidate subgraphs using one or more graph partitioning algorithms. Next, we identify the optimal summarization using the minimum description length MDL principle, picking only those subgraphs from the candidates that together yield the best lossless compression of the graph-or, equivalently, that most succinctly describe its adjacency matrix.

Literature Review:
OLAP and data mining techniques applied to data graphs have considered them through global, aggregated views, looking for statistics and/or trends in the graph structure and content. Yan et al.  leverages pattern-mining techniques to build graph indices in order to help processing a graph query. It firstly applies a frequent pattern-mining algorithm to iden-tify all the frequent patterns with the size support constraint. Once the frequent patterns are extracted, they are organized in a prefix tree structure, where each pattern is associated with a list of ids of the graphs containing it. This prefix tree is then used to answer queries asking for the respective part of the graph. This approach by design does not reflect all data and is based on numeric information. BIB002 uses the same idea, but considers trees instead of graphs. The Vocabulary-based summarization of Graphs (VoG) BIB007 aims at summarizing a graph by its characteristic subgraphs of some fixed types, which have been observed encoding meaningful information in real graphs: cliques, bi-partite cores, stars, chains, and approximations thereof. VoG first decomposes the input graph, using any clustering method, into possibly overlapping subgraphs, the (approximate) type of each is then identified using the Minimum Description Length principle BIB001 . Finally, the input graph summary is composed of some non-redundant subgraphs, picked by some heuristic like top k, hence may not reflect all the input graph. Importantly, the VoG method has been shown to scale well; it is near-linear in the number of edges. The VoG code is available for download. BIB005 An aggregation framework for OLAP operations on labeled graphs is introduced in BIB003 . The authors assume as available an OLAP-style set of dimensions with their hierarchies and measures; in particular, graph topological information is used as aggregation dimensions. Based on these, they define a "graph cube" and investigate efficient methods for computing it. With a different perspective, BIB004 focuses on building out of node-and edge-labeled graphs, a set of randomized summaries, so that one can apply data mining techniques on the summary set instead of the original graph. Using the summary set leads to better performance, while guaranteeing upper bounds on the information loss incurred. A graph summarization approach, based solely on the graph structure is reported in BIB006 . It produces a summary graph that describes the underlying topology characteristics of the original one. Every summary node, or supernode, comprises of a set of nodes from the original graph; every summary edge, or super-edge, represents an all-to-all connections between the nodes in the corresponding supernodes. The goal of this work is to generate a summary that minimizes the false positives and negatives introduced by this summarization. The authors investigate different distributed graph summarization methods, which proceed in an incremental fashion, gradually merging nodes into supernodes; the methods differ in the way they chose the pairs of nodes to be merged, and cut different trade-offs between efficiency (running time) and effectiveness (keeping the false positives and negatives under control). The method termed Dist-LSH selects node pairs with a high probability to be merged; the probability is estimated based on locality-sensitive hashing (LSH) of the nodes. The algorithms are implemented on top of the Apache Giraph framework. Lin et al.  surveys many other quantitative, miningoriented graph sampling and summarization methods.