Fast algorithm for detecting community structure in networks.
TLDR
An algorithm is described which gives excellent results when tested on both computer-generated and real-world networks and is much faster, typically thousands of times faster, than previous algorithms.Abstract:
Many networks display community structure--groups of vertices within which connections are dense but between which they are sparser--and sensitive computer algorithms have in recent years been developed for detecting this structure. These algorithms, however, are computationally demanding, which limits their application to small networks. Here we describe an algorithm which gives excellent results when tested on both computer-generated and real-world networks and is much faster, typically thousands of times faster, than previous algorithms. We give several example applications, including one to a collaboration network of more than 50,000 physicists.read more
Citations
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Analysing ecological networks of species interactions.
Eva Delmas,Eva Delmas,Mathilde Besson,Mathilde Besson,Marie-Hélène Brice,Marie-Hélène Brice,Laura A. Burkle,Giulio Valentino Dalla Riva,Marie-Josée Fortin,Dominique Gravel,Dominique Gravel,Paulo R. Guimarães,David H. Hembry,Erica A. Newman,Erica A. Newman,Jens M. Olesen,Mathias M. Pires,Justin D. Yeakel,Justin D. Yeakel,Timothée Poisot,Timothée Poisot +20 more
TL;DR: An overview of tools that can be used to describe and compare the functional and dynamic roles of species based on their position in the network and the organization of their interactions as well as associated new methods to test the significance of these results are described.
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Method to find community structures based on information centrality.
TL;DR: An algorithm of hierarchical clustering that consists in finding and removing iteratively the edge with the highest information centrality is developed that is very effective especially when the communities are very mixed and hardly detectable by the other methods.
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Detecting emerging research fronts based on topological measures in citation networks of scientific publications
TL;DR: The results showed that topological measures are beneficial in detecting branching innovation in the citation network of scientific publications.
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Community detection in networks: A multidisciplinary review
Muhammad Aqib Javed,Muhammad Shahzad Younis,Siddique Latif,Siddique Latif,Junaid Qadir,Adeel Baig,Adeel Baig +6 more
TL;DR: A contemporary survey on the methods of community detection and its applications in the various domains of real life by reviewing prevailing community detection algorithms that range from traditional algorithms to state of the art algorithms for overlapping community detection.
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A New Measure of Centrality for Brain Networks
TL;DR: A new centrality metric called leverage centrality is proposed that considers the extent of connectivity of a node relative to the connectivity of its neighbors and may be able to identify critical nodes that are highly influential within the network.
References
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Community structure in social and biological networks
Michelle Girvan,Mark Newman +1 more
TL;DR: This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.
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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.