Community detection algorithm evaluation with ground-truth data

TL;DR

This paper proposes a new methodology for evaluating community detection algorithms by analyzing the topological features of community graphs, providing a more comprehensive assessment than traditional node-level metrics.

Contribution

It introduces a novel approach using community graph topology for algorithm evaluation and demonstrates its effectiveness on real-world networks with known community structures.

Findings

Community graph topology offers better insight into algorithm performance.

Traditional metrics are less sensitive to overall community structure variations.

Combining topology-based and classical metrics yields a more complete evaluation.

Abstract

Community structure is of paramount importance for the understanding of complex networks. Consequently, there is a tremendous effort in order to develop efficient community detection algorithms. Unfortunately, the issue of a fair assessment of these algorithms is a thriving open question. If the ground-truth community structure is available, various clustering-based metrics are used in order to compare it versus the one discovered by these algorithms. However, these metrics defined at the node level are fairly insensitive to the variation of the overall community structure. To overcome these limitations, we propose to exploit the topological features of the 'community graphs' (where the nodes are the communities and the links represent their interactions) in order to evaluate the algorithms. To illustrate our methodology, we conduct a comprehensive analysis of overlapping community detection algorithms using a set of real-world networks with known a priori community structure. Results provide a better perception of their relative performance as compared to classical metrics. Moreover, they show that more emphasis should be put on the topology of the community structure. We also investigate the relationship between the topological properties of the community structure and the alternative evaluation measures (quality metrics and clustering metrics). It appears clearly that they present different views of the community structure and that they must be combined in order to evaluate the effectiveness of community detection algorithms.