Local community detection in multilayer networks

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.

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Notes

  1. 1.

    Source code and evaluation data are available at http://people.dimes.unical.it/andreatagarelli/mllcd/

  2. 2.

    https://www.theia-land.fr/en/products/spot-world-heritage

  3. 3.

    As specified in the publicly available implementation from the repository at https://github.com/YixuanLi/LEMON

  4. 4.

    Experiments were carried out on an Intel Core i7-3960X CPU @3.30GHz, 64GB RAM machine. All our algorithms are written in Python. Original codes of competing methods are in Python (\(\textsf {LART} \)) and Matlab (\(\textsf {PMM} \) and \(\textsf {GL} \)).

  5. 5.

    We do not include DBLP in this evaluation because the competitors incurred out-of-memory issues, though our methods did not.

  6. 6.

    For \(\textsf {LART} \) and \(\textsf {GL} \) we report only one performance score since they have a deterministic behavior.

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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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Correspondence to Andrea Tagarelli.

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Responsible editor: Kurt Driessens, Dragi Kocev, Marko Robnik-Šikonja Myra Spiliopoulou.

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Interdonato, R., Tagarelli, A., Ienco, D. et al. Local community detection in multilayer networks. Data Min Knowl Disc 31, 1444–1479 (2017). https://doi.org/10.1007/s10618-017-0525-y

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Keywords

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