Local method for detecting communities.

By James P. Bagrow, Erik M. Bollt. Physical review. E, Statistical, nonlinear, and soft matter physics, 2004

304 citations recorded by the source corpus.

We propose a method of community detection that is computationally inexpensive and possesses physical significance to a member of a social network. This method is unlike many divisive and agglomerative techniques and is local in the sense that a community can be detected within a network without requiring knowledge of the entire network. A global application of this method is also introduced. Several artificial and real-world networks, including the famous Zachary karate club, are analyzed.

Subfields: Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/781957632d2ba3c4e8a2d15cd5b7e970782bb355

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Statistical and Nonlinear Physics

Citations in the corpus

Finding local community structure in networks. (2005, background): “Recently, another approach to inferring community structure using only local information appeared [24].”
Communities, modules and large-scale structure in networks (2011, unknown): “A contrasting set of approaches focuses on the detection of ‘local communities’ 23,24 and seeks to answer the question of whether we can, given a single node, identify the community to which it belongs, without first finding all communities in the network.”
Statistical properties of community structure in large social and information networks (2008, background): “Other recent work that has focused on developing local and/or near-linear time heuristics for community detection include [48, 155, 46, 21, 134].”
Comparing community structure identification (2005, methodology): “ich belong to 19 Reference Alias Order [25] NG O(m2n) [11] GN O(n2m) [29] FLM O(n4) [23] RCCLP O(n2) [35] NF O(nlog2n) [43], DMSA O(n3) [43], DMCA O(n3) [13] EM O(mhk2i) [14] ZL O(n3) [54] RB unknown [34] BB O(n3) [38] DA O(n2logn) [45] CSCC O(n2) [47] WH O(n+m) Table 1: Table summarising how diﬀere”
Community detection in graphs (2009, methodology): “Bagrow and Bollt designed an agglomerative technique, called L-shell method (Bagrow and Bollt, 2005).”
Comparing Community Structure to Characteristics in Online Collegiate Social Networks (2008, methodology): “One of the presently dominant approaches in community detection involves the optimization of a quality function known as modularity, which counts the total edge weight of all intra-community connections compared to that which might be expected at random (under some null model) [27,71,73,74,76], thou”
Community Structure in Graphs (2007, methodology): “This is an agglomerative method designed by Bagrow and Bollt [87].”
Characterization of complex networks: A survey of measurements (2005, methodology): “s More recently, some methods have been developed to detect the local community of a vertex based only on local information about the network topology. One such method was proposed by Bagrow and Bolt [145], which is based on the change of the hierarchical degree between two consecutive distances (se”
A classification for community discovery methods in complex networks (2011, methodology): “e a bridge between communities. i.e. a general approach that uses split betweenness in order to obtain an overlapping community discovery framework [35]. We then also consider two alternative methods [36, 37] which try to detect the bridges by expanding the community structure and computing a commun”
Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters (2008, background): “Other recent work that has focused on developing local and/or near-linear time heuristics for community detection include [49, 158, 47, 21, 137].”
DEMON: a local-first discovery method for overlapping communities (2012, background): “A related work is also [2], whose aim is also to discover local communities.”
A DIseAse MOdule Detection (DIAMOnD) Algorithm Derived from a Systematic Analysis of Connectivity Patterns of Disease Proteins in the Human Interactome (2015, methodology): “Numerous algorithms [14–23] define a DIAMOnD and Disease Modules within the Human Interactome”