Impact of network topology on efficiency of proximity measures for community detection

TL;DR

This paper investigates how different network topologies influence the effectiveness of various proximity measures used in community detection, highlighting that measure performance varies with network structure but some measures are consistently reliable.

Contribution

It systematically evaluates the performance of proximity measures across diverse network topologies, revealing the dependency of measure effectiveness on network structure and identifying robust measures.

Findings

Measure performance varies with network topology.

Some measures perform well across most topologies.

Network structure significantly influences community detection efficiency.

Abstract

Many community detection algorithms require the introduction of a measure on the set of nodes. Previously, a lot of efforts have been made to find the top-performing measures. In most cases, experiments were conducted on several datasets or random graphs. However, graphs representing real systems can be completely different in topology: the difference can be in the size of the network, the structure of clusters, the distribution of degrees, the density of edges, and so on. Therefore, it is necessary to explicitly check whether the advantage of one measure over another is preserved for different network topologies. In this paper, we consider the efficiency of several proximity measures for clustering networks with different structures. The results show that the efficiency of measures really depends on the network topology in some cases. However, it is possible to find measures that behave well for most topologies.