Evaluating Community Detection Algorithms for Progressively Evolving Graphs

TL;DR

This paper introduces a novel benchmark generator for dynamic graphs with evolving community structures and evaluates six algorithms, revealing strengths and weaknesses in their performance and smoothness handling.

Contribution

It presents a flexible benchmark generator for dynamic graphs with specified evolving communities and provides a comprehensive empirical comparison of existing algorithms.

Findings

Different algorithms exhibit specific weaknesses like glitches and oversimplification.

No single algorithm is best in all metrics; trade-offs exist.

The study identifies the fastest, most smoothed, and most accurate algorithms.

Abstract

Many algorithms have been proposed in the last ten years for the discovery of dynamic communities. However, these methods are seldom compared between themselves. In this article, we propose a generator of dynamic graphs with planted evolving community structure, as a benchmark to compare and evaluate such algorithms. Unlike previously proposed benchmarks, it is able to specify any desired evolving community structure through a descriptive language, and then to generate the corresponding progressively evolving network. We empirically evaluate six existing algorithms for dynamic community detection in terms of instantaneous and longitudinal similarity with the planted ground truth, smoothness of dynamic partitions, and scalability. We notably observe different types of weaknesses depending on their approach to ensure smoothness, namely Glitches, Oversimplification and Identity loss. Although no method arises as a clear winner, we observe clear differences between methods, and we identified the fastest, those yielding the most smoothed or the most accurate solutions at each step.