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High quality, scalable and parallel community detection for large real graphs

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10.1145/2566486.2568010
 
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hdl:2117/27168

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Prat Pérez, Arnau
Domínguez Sal, David
Larriba Pey, JosepMés informacióMés informacióMés informació
Document typeConference report
Defense date2014
PublisherAssociation for Computing Machinery (ACM)
Rights accessRestricted access - publisher's policy
Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
Community detection has arisen as one of the most relevant topics in the field of graph mining, principally for its applications in domains such as social or biological networks analysis. Different community detection algorithms have been proposed during the last decade, approaching the problem from different perspectives. However, existing algorithms are, in general, based on complex and expensive computations, making them unsuitable for large graphs with millions of vertices and edges such as those usually found in the real world. In this paper, we propose a novel disjoint community detection algorithm called Scalable Community Detection (SCD). By combining different strategies, SCD partitions the graph by maximizing the Weighted Community Clustering (WCC), a recently proposed community detection metric based on triangle analysis. Using real graphs with ground truth overlapped communities, we show that SCD outperforms the current state of the art proposals (even those aimed at finding overlapping communities) in terms of quality and performance. SCD provides the speed of the fastest algorithms and the quality in terms of NMI and F1Score of the most accurate state of the art proposals. We show that SCD is able to run up to two orders of magnitude faster than practical existing solutions by exploiting the parallelism of current multi-core processors, enabling us to process graphs of unprecedented size in short execution times.
CitationPrat, A.; Dominguez, D.; Larriba, J. High quality, scalable and parallel community detection for large real graphs. A: International World Wide Web Conference. "WWW '14: proceedings of the 23rd International Conference on World Wide Web". Seoul: Association for Computing Machinery (ACM), 2014, p. 225-236. 
URIhttp://hdl.handle.net/2117/27168
DOI10.1145/2566486.2568010
ISBN978-1-4503-2744-2
Publisher versionhttp://wwwconference.org/proceedings/www2014/starthere.htm
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  • DAMA-UPC - Data Management Group de la Universitat Politècnica de Catalunya - Ponències/Comunicacions de congressos [21]
  • Departament d'Arquitectura de Computadors - Ponències/Comunicacions de congressos [1.773]
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