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: On Using Network Science in Mining Developers Collaboration in Software Engineering: A Systematic Literature Review 
Chapter Title:  Weighted networks: 
1. Abstract of Cited Paper (BIB001):  The connections in many networks are not merely binary entities, either present or not, but have associated weights that record their strengths relative to one another. Recent studies of networks have, by and large, steered clear of such weighted networks, which are often perceived as being harder to analyze than their unweighted counterparts. Here we point out that weighted networks can in many cases be analyzed using a simple mapping from a weighted network to an unweighted multigraph, allowing us to apply standard techniques for unweighted graphs to weighted ones as well. We give a number of examples of the method, including an algorithm for detecting community structure in weighted networks and a simple proof of the maximum-flow--minimum-cut theorem. 
2. Abstract of Cited Paper (BIB002):  Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link-prediction problem, and we develop approaches to link prediction based on measures for analyzing the “proximity” of nodes in a network. Experiments on large coauthorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures. © 2007 Wiley Periodicals, Inc. 
3. Abstract of Cited Paper (BIB003):  Networks have become a key approach to understanding systems of interacting objects, unifying the study of diverse phenomena including biological organisms and human society. One crucial step when studying the structure and dynamics of networks is to identify communities: groups of related nodes that correspond to functional subunits such as protein complexes or social spheres. Communities in networks often overlap such that nodes simultaneously belong to several groups. Meanwhile, many networks are known to possess hierarchical organization, where communities are recursively grouped into a hierarchical structure. However, the fact that many real networks have communities with pervasive overlap, where each and every node belongs to more than one group, has the consequence that a global hierarchy of nodes cannot capture the relationships between overlapping groups. Here we reinvent communities as groups of links rather than nodes and show that this unorthodox approach successfully reconciles the antagonistic organizing principles of overlapping communities and hierarchy. In contrast to the existing literature, which has entirely focused on grouping nodes, link communities naturally incorporate overlap while revealing hierarchical organization. We find relevant link communities in many networks, including major biological networks such as protein-protein interaction and metabolic networks, and show that a large social network contains hierarchically organized community structures spanning inner-city to regional scales while maintaining pervasive overlap. Our results imply that link communities are fundamental building blocks that reveal overlap and hierarchical organization in networks to be two aspects of the same phenomenon. 
4. Abstract of Cited Paper (BIB004):  Abstract Ties often have a strength naturally associated with them that differentiate them from each other. Tie strength has been operationalized as weights. A few network measures have been proposed for weighted networks, including three common measures of node centrality: degree, closeness, and betweenness. However, these generalizations have solely focused on tie weights, and not on the number of ties, which was the central component of the original measures. This paper proposes generalizations that combine both these aspects. We illustrate the benefits of this approach by applying one of them to Freeman’s EIES dataset. 
5. Abstract of Cited Paper (BIB005):  Tie formation in social networks is driven by different motives that are not always apparent in the social network itself. These motives differ from one social network to another, depending on, e.g., the network's purpose, such as advice seeking or collaboration, and the effort it costs to establish a friendship relationship. A common factor that exists in almost all social networks is homophily: the tendency of social network members to connect to similar members. In this work, we look at the tie formation process in social networks from a different perspective where we consider not only a social network SN, but also a set of associated interaction networks G n around it. We show, based on 6 social networks and in total 20 different associated interaction networks, that it is possible to predict the entire social network's structure to a satisfactory extent, only by knowing the structure of these interaction networks. As social networks are based on a voluntary relationship while some of the interaction relationships are at most semi-controllable for most members, e.g., being together in a team, this seems to indicate that whom we choose as a friend is also determined by whom we interact with. 
6. Abstract of Cited Paper (BIB006):  Many complex network systems suffer from noise that disguises the structure of the network and hinders an accurate analysis of these systems. Link assessment is the process of identifying and eliminating the noise from network systems in order to better understand these systems. In this paper, we address the link assessment problem in social networks that may suffer from noisy relationships. We employed a machine learning classifier for assessing the links in the social network of interest using the data from the associated interaction networks around it. The method was tested with two different data sets: each contains the social network of interest, with ground truth, along with the associated interaction networks. The results showed that it is possible to effectively assess the links of a social network using only the structure of a single network of the associated interaction networks and also using the structure of the whole set of the associated interaction networks. The experiment also revealed that the assessment performance using only the structure of the social network of interest is relatively less accurate than using the associated interaction networks. This indicates that link formation in the social network of interest is not only driven by the internal structure of the social network, but also influenced by the external factors provided in the associated interaction networks. 
7. Abstract of Cited Paper (BIB007):  Online social networks (OSNs) have become the main medium for connecting people, sharing knowledge and information, and for communication. The social connections between people using these OSNs are formed as virtual links (e.g., friendship and following connections) that connect people. These links are the heart of today's OSNs as they facilitate all of the activities that the members of a social network can do. However, many of these networks suffer from noisy links, i.e., links that do not reflect a real relationship or links that have a low intensity, that change the structure of the network and prevent accurate analysis of these networks. Hence, a process for assessing and ranking the links in a social network is crucial in order to sustain a healthy and real network. Here, we define link assessment as the process of identifying noisy and non-noisy links in a network. In this paper, we address the problem of link assessment and link ranking in social networks using external interaction networks. In addition to a friendship social network, additional exogenous interaction networks are utilized to make the assessment process more meaningful. We employed machine learning classifiers for assessing and ranking the links in the social network of interest using the data from exogenous interaction networks. The method was tested with two different datasets, each containing the social network of interest, with the ground truth, along with the exogenous interaction networks. The results show that it is possible to effectively assess the links of a social network using only the structure of a single network of the exogenous interaction networks, and also using the structure of the whole set of exogenous interaction networks. The experiments showed that some classifiers do better than others regarding both link classification and link ranking.

Literature Review:
The collaboration between developers and the communication between them can hardly be imagined without considering the intensity (or frequency) of this collaboration or communication, which is a rich information that should be utilized. The work by Opsahl BIB005 and by Newman BIB002 provide methods for bipartite and weighted networks and their corresponding centrality measures. 3. Community detection: The PSs that handle community detection were based on the work of Newman BIB001 . A recent work by Ahn et al. BIB004 showed promising results for finding structural properties in networks based on the link communities. This method could provide more robust team organizations with the collaborative development. 4. Link prediction and assessment: The prediction of collaboration or communication between developers was, surprisngly, not found in the PSs. In network science there is a plethora of work in link prediction started by Liben-Nowell and Kleinberg BIB003 . The link prediction is an active area in network science and can be utilized in mining software repositories, especially when incorporating external information BIB006 BIB007 BIB008 . An application of the link prediction in this context could be built as recommendation system for bringing developers to projects in open source.