2020
Report  Open Access

Evaluating Community Detection Algorithms for Progressively Evolving Graphs

Cazabet R., Boudebza S., Rossetti G.

complex networks  community structure  benchmark 

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.

Source: ISTI Technical Reports 2020/013, 2020, 2020



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CNR authors

Rossetti, Giulio

Projects (via OpenAIRE)

SoBigData-PlusPlus
SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics


OpenAIRE
BibTeX entry
@techreport{oai:it.cnr:prodotti:439432,
	title = {Evaluating Community Detection Algorithms for Progressively Evolving Graphs},
	author = {Cazabet R. and Boudebza S. and Rossetti G.},
	doi = {10.32079/isti-tr-2020/013},
	institution = {ISTI Technical Reports 2020/013, 2020, 2020},
	year = {2020}
}