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Data Mining and Knowledge Discovery

, Volume 31, Issue 5, pp 1444–1479 | Cite as

Local community detection in multilayer networks

  • Roberto Interdonato
  • Andrea Tagarelli
  • Dino Ienco
  • Arnaud Sallaberry
  • Pascal Poncelet
Article
Part of the following topical collections:
  1. Journal Track of ECML PKDD 2017

Abstract

The problem of local community detection in graphs refers to the identification of a community that is specific to a query node and relies on limited information about the network structure. Existing approaches for this problem are defined to work in dynamic network scenarios, however they are not designed to deal with complex real-world networks, in which multiple types of connectivity might be considered. In this work, we fill this gap in the literature by introducing the first framework for local community detection in multilayer networks (ML-LCD). We formalize the ML-LCD optimization problem and provide three definitions of the associated objective function, which correspond to different ways to incorporate within-layer and across-layer topological features. We also exploit our framework to generate multilayer global community structures. We conduct an extensive experimentation using seven real-world multilayer networks, which also includes comparison with state-of-the-art methods for single-layer local community detection and for multilayer global community detection. Results show the significance of our proposed methods in discovering local communities over multiple layers, and also highlight their ability in producing global community structures that are better in modularity than those produced by native global community detection approaches.

Keywords

Node-centric community detection Personalized community detection Complex systems Multiplex networks 

Notes

Acknowledgements

D. Ienco would like to acknowledge the National French Center for Spatial Study (CNES), in the framework of the project “DYNAMITEF TOSCA 2016”, to support this study with the RemoteSensing dataset.

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Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.DIMES - University of CalabriaRendeItaly
  2. 2.IRSTEAUMR TETISMontpellierFrance
  3. 3.LIRMMUniversité Paul ValéryMontpellierFrance
  4. 4.LIRMMUniversité de MontpellierMontpellierFrance

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