Communities in Networks

By M. A. Porter, Jukka-Pekka Onnela, Peter J. Mucha. arXiv.org, 2009

1,108 citations recorded by the source corpus.

We survey some of the concepts, methods, and applications of community detection, which has become an increasingly important area of network science. To help ease newcomers into the field, we provide a guide to available methodology and open problems, and discuss why scientists from diverse backgrounds are interested in these problems. As a running theme, we emphasize the connections of community detection to problems in statistical physics and computational optimization.

Subfields: Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/beecc2eb2b2a9754668110d07ab98e52d870ad28

How researchers see this paper

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

In Experimental and Cognitive Psychology

In Statistical and Nonlinear Physics

Citations in the corpus

Comparing Community Structure to Characteristics in Online Collegiate Social Networks (2008, background): “Myriad papers have attempted to interpret such organization through the computation of structural modules or communities [8, 33], which are defined in terms of mesoscopic groups of nodes with more internal connections (between nodes in the group) than external connections (between nodes in the group”
Mathematical Formulation of Multilayer Networks (2013, background): “There are many ways to maximize modularity as well as many other ways to algorithmically detect communities (see the review articles [59,60]).”
Random Walks, Markov Processes and the Multiscale Modular Organization of Complex Networks (2008, background): “Most of these studies follow from the classical problem of graph partitioning and are thus based on structural properties of graphs [6], [7].”
Core-Periphery Structure in Networks (2012, methodology): “ture [21, 51], in which cohesive groups called “communities” consist of nodes that are connected densely to each other and the connections between nodes in different communities are comparatively sparse.”
Multilayer networks (2013, methodology): “Another important (and old [269]) method for community detection in monoplex networks is spectral clustering.”
Community detection in Social Media (2012, methodology): “For a more thorough discussion of their principles, we refer to the survey articles by Danon et al. (2005), Fortunato and Castellano (2007), Fortunato (2009, 2010), Porter et al. (2009), and Schaeffer (2007).”
Discovering social circles in ego networks (2012, background): “Although a circle is not precisely the same as a community, our work broadly falls under the umbrella of community detection [Lancichinetti and Fortunato 2009b; Schaeffer 2007; Leskovec et al. 2010; Porter et al. 2009; Newman 2004].”
Generalized Louvain method for community detection in large networks (2011, background): “There exist numerous comprehensive surveys to this problem, such as [2], [3].”
Modular and Hierarchically Modular Organization of Brain Networks (2010, methodology): “Literally hundreds of methods have been proposed (Porter et al., 2009; Fortunato, 2010), differing in their time complexity and their notion of what a community is.”
Community detection in graphs (2009, neutral): “(Danon et al., 2007; Fortunato and Castellano, 2009; Newman, 2004a; Porter et al., 2009; Schaeffer, 2007)).”
A survey of community search over big graphs (2019, methodology): “rtex’s degree is 3 within the subgraph, and it is also the core attaining the maximum value of k. In the literature, there is a highly related group of research works, called community detection (CD) [159, 45,155,157,111]. Generally, it has similar goals with CS, but there are three key dierences: (”
A classification for community discovery methods in complex networks (2011, background): “We would consider the most important to be [176, 177, 178, 179, 180, 181].”
The structure and dynamics of multilayer networks (2014, background): “These communities are disjoint groups (sets) of nodes which are more densely connected to each other than they are to the rest of the network [12,99,100].”