Towards Direct Comparison of Community Structures in Social Networks
This work addresses the need for more direct evaluation methods in social network analysis, though it appears incremental as it builds on existing community detection frameworks.
The paper tackles the problem of evaluating community detection algorithms by proposing a direct comparison approach that uses topological information of communities, introducing a quality measure called Topological Variance (TV) and two ranking schemes, and demonstrates their efficacy on eight real-world datasets and six algorithms.
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.