Metrics for Community Analysis: A Survey
Tanmoy Chakraborty, Ayushi Dalmia, Animesh Mukherjee, Niloy Ganguly

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…
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Taxonomy
TopicsComplex Network Analysis Techniques · Opinion Dynamics and Social Influence · Human Mobility and Location-Based Analysis
