The many facets of community detection in complex networks.
Michael T. Schaub,Michael T. Schaub,Michael T. Schaub,Jean-Charles Delvenne,Martin Rosvall,Renaud Lambiotte +5 more
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In this paper, the authors provide a focused review of the different motivations that underpin community detection, highlighting the different facets of community detection and highlighting the many lines of research and points out open directions and avenues for future research.Abstract:
Community detection, the decomposition of a graph into essential building blocks, has been a core research topic in network science over the past years. Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular form of assortative community structure and classified based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different goals and reasons for why we want to employ community detection in the first place. Here we provide a focused review of these different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different facets of community detection also delineates the many lines of research and points out open directions and avenues for future research.read more
Citations
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Journal ArticleDOI
A Framework for the Construction of Generative Models for Mesoscale Structure in Multilayer Networks
Marya Bazzi,Marya Bazzi,Marya Bazzi,Lucas G. S. Jeub,Lucas G. S. Jeub,Lucas G. S. Jeub,Alex Arenas,Sam Howison,Mason A. Porter,Mason A. Porter +9 more
TL;DR: In this article, the authors introduce a framework for the construction of generative models for mesoscale structures in multilayer networks, such as dense sets of nodes known as communities, to discover network features that are not apparent at the microscale or the macroscale.
Book ChapterDOI
Community Detection Methods in Social Network Analysis
Iliho,Sri Khetwat Saritha +1 more
TL;DR: This paper will present the different ways in which one can discover the communities existing in social network graphs based on several community detection methods.
Proceedings ArticleDOI
MCD: Mutually Connected Community Detection using clustering coefficient approach in social networks
TL;DR: Experimental results demonstrated that components having a higher value of clustering coefficient are strongly connected and form a community of mutual connectivity and the effectiveness and the efficiency of the proposed MCD(Mutual Community Detection) is proposed.
Journal ArticleDOI
Network community detection using modularity density measures
TL;DR: In this article, a new metric, modularity density, was introduced for the quality of community structure in networks in order to solve some of the known problems with modularity, particularly the resolution limit problem.
Journal ArticleDOI
A Node Similarity and Community Link Strength-Based Community Discovery Algorithm
TL;DR: In this article, a community detection method named NSCLS was proposed to detect high quality community structures from various networks, and its results were much better than the counterparts of comparison algorithms.
References
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Journal ArticleDOI
Normalized cuts and image segmentation
Jianbo Shi,Jitendra Malik +1 more
TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.
Journal ArticleDOI
Fast unfolding of communities in large networks
Vincent D. Blondel,Jean-Loup Guillaume,Jean-Loup Guillaume,Renaud Lambiotte,Renaud Lambiotte,Etienne Lefebvre +5 more
TL;DR: This work proposes a heuristic method that is shown to outperform all other known community detection methods in terms of computation time and the quality of the communities detected is very good, as measured by the so-called modularity.
Journal ArticleDOI
Finding and evaluating community structure in networks.
TL;DR: It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems.
Journal ArticleDOI
Fast unfolding of communities in large networks
Vincent D. Blondel,Jean-Loup Guillaume,Jean-Loup Guillaume,Renaud Lambiotte,Renaud Lambiotte,Etienne Lefebvre +5 more
TL;DR: In this paper, the authors proposed a simple method to extract the community structure of large networks based on modularity optimization, which is shown to outperform all other known community detection methods in terms of computation time.