Metrics for Community Analysis: A Survey

TL;DR

This paper provides a comprehensive survey of metrics used for community detection and evaluation in social and information networks, highlighting their applications, differences, and effectiveness through experiments on various networks.

Contribution

It offers the first organized, detailed overview of community metrics and compares their performance on synthetic and real-world networks.

Findings

Metrics vary in effectiveness depending on network type

Overlapping and hierarchical communities require specialized metrics

Experimental results highlight strengths and limitations of different metrics

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.