Towards Direct Comparison of Community Structures in Social Networks

TL;DR

This paper introduces a novel method for directly comparing community structures in social networks by using a new topological variance measure, enabling more accurate evaluation of community detection algorithms.

Contribution

It proposes a direct comparison approach for community structures using the Topological Variance measure, addressing limitations of existing evaluation metrics.

Findings

The Topological Variance measure effectively captures differences in community topologies.

The proposed ranking schemes outperform traditional metrics in evaluating community detection algorithms.

Experiments on real-world datasets validate the utility of the new comparison method.

Abstract

Community detection algorithms are in general evaluated by comparing evaluation metric values for the communities obtained with different algorithms. The evaluation metrics that are used for measuring quality of the communities incorporate the topological information of entities like connectivity of the nodes within or outside the communities. However, while comparing the metric values it loses direct involvement of topological information of the communities in the comparison process. In this paper, a direct comparison approach is proposed where topological information of the communities obtained with two algorithms are compared directly. A quality measure namely \emph{Topological Variance (TV)} is designed based on direct comparison of topological information of the communities. Considering the newly designed quality measure, two ranking schemes are developed. The efficacy of proposed quality metric as well as the ranking scheme is studied with eight widely used real-world datasets and six community detection algorithms.