Detecting community structure in networks

By Mark Newman. 2004

654 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/8c38fe60b3761e25ed2d2486c3a944674a3e9534

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In Computational Theory and Mathematics

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In Statistical and Nonlinear Physics

Citations in the corpus

Distributed community detection in delay tolerant networks (2007, methodology): “Community structures are usually substructures/subgraphs corresponding to important functions, and examples can be found in many areas, such as World Wide Web [7], biological networks [9], social networks [21], and also the Internet [18].”
Investor Networks in the Stock Market (2014, positive): “In other words, investor 1 is more central than the other investors and is therefore expected to have higher trading profits (Ozsoylev and Walden (2011)).”
Centrality measures and the importance of generalist species in pollination networks (2010, background): “Nodes with BC > 0 connect areas of the network that would otherwise be sparsely or not connected at all (Newman, 2004).”
Age-related changes in modular organization of human brain functional networks (2009, neutral): “…Cambridge, UK b Behavioural and Clinical Neurosciences Institute, University of Cambridge, Cambridge, UK c Grenoble Image Parole Signal Automatique, Centre National de la Recherche Scientifique, Grenoble, France d Centre for Cognitive and Neural Systems, University of Edinburgh, Edinburgh,…”
Statistical properties of community structure in large social and information networks (2008, background): “For example, see the reviews on community identification [24, 9], graph and spectral clustering [13, 30], and the monographs on spectral graph theory and complex networks [6, 7].”
Non-overlapping community detection (2018, methodology): “FastGreedy is an agglomerative hierarchical clustering method developed by Newman et al. (Newman 2004b).”
Robustness of community structure in networks. (2007, neutral): “This finding suggests that in networked systems whose function is less well understood we may be able to gain insight by discovering and examining their communities (if any), and methods for community discovery have, as a result, attracted a substantial amount of attention in the recent literature i”
A review of modularization techniques in artificial neural networks (2019, methodology): “In more recent approaches (Newman, 2004; Tyler et al., 2005; Radicchi et al., 2004), a modularity measure (Equation 1) was used to guide the detection process towards the best division.”
Link mining: a survey (2005, methodology): “Spectral graph partitioning methods address the group detection problem by identifying an approximately minimal set of links to remove from the graph to achieve a given number of groups [82; 30].”
Finding Statistically Significant Communities in Networks (2010, neutral): “Vertices have essentially the same degree and communities have the same size, at odds with empirical analysis showing that both features typically are broadly distributed [19,44–48].”
Modularity-maximizing graph communities via mathematical programming (2007, background): “For a survey, see [35].”
Finding community structure in networks using the eigenvectors of matrices. (2006, methodology): “Social networks [5], biochemical networks [7, 8, 9], and information networks such as the web [10], have all been shown to possess strong community structure, a ﬁnding that has substantial practical implications for our understanding of the systems these networks represent.”
Like like alike — Joint Friendship and Interest Propagation in Social Networks Shuang (2011, methodology): “Some of them employ random walk methods [14] [22] or spectral graph algorithms [19] [20].”
Consistency of community detection in networks under degree-corrected stochastic block models (2011, background): “A number of greedy algorithms such as hierarchical clustering have been proposed (see [22] for a review), which we will not focus on in this paper.”