Metrics for Community Analysis

Abstract

Detecting and analyzing dense groups or communities from social and information networks has attracted immense attention over last one decade due to its enormous applicability in different domains. Community detection is an ill-defined problem, as the nature of the communities is not known in advance. The problem has turned out to be even complicated due to the fact that communities emerge in the network in various forms -disjoint, overlapping, hierarchical etc. Various heuristics have been proposed depending upon the application in hand. All these heuristics have been materialized in the form of new metrics, which in most cases are used as optimization functions for detecting the community structure, or provide an indication of the goodness of detected communities during evaluation. There arises a need for an organized and detailed survey of the metrics proposed with respect to community detection and evaluation. In this survey, we present a comprehensive and structured overview of the start-of-the-art metrics used for the detection and the evaluation of community structure. We also conduct experiments on synthetic and real-world networks to present a comparative analysis of these metrics in measuring the goodness of the underlying community structure.

Community detection in networks: A user guide

“…Also, we discuss some popular classes of algorithms and give advice on their usage. More info on network clustering can be found in several review articles (Chakraborty et al, 2016;Coscia et al, 2011;Malliaros and Vazirgiannis, 2013;Parthasarathy et al, 2011;Porter et al, 2009;Schaeffer, 2007;Xie et al, 2013).…”

Demarcating geographic regions using community detection in commuting networks with significant self-loops

“…We compare our results with other existing delineations (in particular OMB's Metropolitan areas) by using the Fuzzy Rand Index (FRI) (Chakraborty et al, 2017). The Fuzzy Rand Index is a metric that measures the similarity of two covers, C 1 and C 2 are.…”

A novel metric for community detection

“…Research into community detection has recently attracted increasing attention (Clauset, Newman, and Moore 2004;Li, Ng, and Ye 2013;Chakraborty et al 2016;Fortunato and Hric 2016). Meanwhile, the design of metrics to evaluate community detection has also been a recent focus of investigation (Newman and Girvan 2004;Fortunato and Barthelemy 2007;Lancichinetti, Fortunato, and Radicchi 2008;Chakraborty et al 2017). The metrics of community structure can be generally grouped within three classes: (1) based on the density of internal links (links within the community) (Radicchi et al 2004); (2) based on the density of external links (Radicchi et al 2004;Fortunato and Hric 2016); and, (3) based on the the density of both internal links and external links (Newman and Girvan 2004).…”

“…Hence, based on different kinds of real-world communities, we argue an intuitive generalisation of the existing definition of community structure. Girvan 2004;Newman 2006b) and its associated generalisations and extensions (Fortunato and Barthelemy 2007;Chakraborty et al 2017) are also based on the preceding density assumption. In this paper we focus on the fact that links within a community are more predictable than external links (Yan and Gregory 2012;Cannistraci, Alanis-Lobato, and Ravasi 2013;Ding et al 2016) -due simply to the assumption that relationships within a real community are stronger (and perhaps more structured) than the external relationships.…”