A Comparative Study of Community Detection Techniques for Large Evolving Graphs

Coppens, Lauranne; Venter, Jonathan De; Mitrović, Sandra; Weerdt, Jochen De

doi:10.1007/978-3-030-43823-4_31

Cited by 1 publication

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References 60 publications

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“…To the best of our knowledge, a single paper has been published so far comparing empirically dynamic community detection algorithms: in [7], 5 methods have been tested on RDyn benchmark [28]. They were compared in terms of average community quality at each step.…”

Section: Scenario Description Edges Generationmentioning

confidence: 99%

Evaluating Community Detection Algorithms for Progressively Evolving Graphs

Cazabet¹,

Boudebza²,

Rossetti³

2020

Preprint

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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.

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Section: Scenario Description Edges Generationmentioning

confidence: 99%