Exploring the limits of community detection strategies in complex networks

TL;DR

This study evaluates various community detection algorithms in complex networks using novel benchmarks and hierarchical clustering, revealing no single algorithm excels universally but combining methods with Surprise maximization yields near-optimal results.

Contribution

Introduces complex closed benchmarks and hierarchical clustering analysis to assess and visualize community detection algorithm performance in networks.

Findings

No algorithm is universally optimal across all networks.

Surprise maximization with multiple algorithms achieves near-optimal results.

Hierarchical clustering helps visualize differences among solutions.

Abstract

The characterization of network community structure has profound implications in several scientific areas. Therefore, testing the algorithms developed to establish the optimal division of a network into communities is a fundamental problem in the field. We performed here a highly detailed evaluation of community detection algorithms, which has two main novelties: 1) using complex closed benchmarks, which provide precise ways to assess whether the solutions generated by the algorithms are optimal; and, 2) A novel type of analysis, based on hierarchically clustering the solutions suggested by multiple community detection algorithms, which allows to easily visualize how different are those solutions. Surprise, a global parameter that evaluates the quality of a partition, confirms the power of these analyses. We show that none of the community detection algorithms tested provide consistently optimal results in all networks and that Surprise maximization, obtained by combining multiple algorithms, obtains quasi-optimal performances in these difficult benchmarks.