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IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

ASONAM 2017: From Security to Community Detection in Social Networking Platforms pp 45–78Cite as

On Detecting Multidimensional Communities

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Part of the book series: Lecture Notes in Social Networks ((LNSN))

Abstract

The study of multidimensional networks has become an active field of research in the last few years. One of the most fundamental tasks is community detection where the aim is to find subsets of densely connected or highly interactive nodes. Community detection in multidimensional networks has particularly gained a lot of attention and a number of approaches have been proposed. Still, several aspects remain to be addressed in the current literature. In fact, besides being parameter-laden, the majority of the proposed approaches thus far lack an outlier detection mechanism and systematic procedures for explicit selection of the dimensions associated with the detected communities. To cope with these limitations, we introduce a novel principled approach named MCDA: multidimensional community detection algorithm. The proposed approach comprises two phases: (1) handling outliers and (2) mining multidimensional communities. The first phase of the algorithm is based on a probabilistic approach that exploits the beta mixture model to identify and eliminate outlier nodes from a network in a systematic way. The second phase adopts a local search mechanism which is inspired from the label propagation principle to detect communities. To this end, we design a novel propagation rule that exploits the most frequently used interaction dimensions among neighbors as an additional constraint for membership selections. The new propagation rule allows MCDA to automatically unfold the hidden communities in a multidimensional context. The detected communities are further processed for selection of relevant dimensions using an inter-class inertia-based procedure.

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Acknowledgements

This work is supported by research grants from the Natural Sciences and Engineering Research Council of Canada (NSERC).

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Authors and Affiliations

  1. Department of Computer Science, University of Quebec at Montreal, Montreal, QC, Canada

    Amani Chouchane, Oualid Boutemine & Mohamed Bouguessa

Authors
  1. Amani Chouchane
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  2. Oualid Boutemine
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  3. Mohamed Bouguessa
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Corresponding author

Correspondence to Mohamed Bouguessa .

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Editors and Affiliations

  1. Department of Informatics & Computers, Hellenic Air Force Academy, Dekelia, Greece

    Panagiotis Karampelas

  2. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Jalal Kawash

  3. Department of Computer Engineering, TOBB University of Economics and Technology, Ankara, Turkey

    Tansel Özyer

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Chouchane, A., Boutemine, O., Bouguessa, M. (2019). On Detecting Multidimensional Communities. In: Karampelas, P., Kawash, J., Özyer, T. (eds) From Security to Community Detection in Social Networking Platforms. ASONAM 2017. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-11286-8_3

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  • DOI: https://doi.org/10.1007/978-3-030-11286-8_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-11285-1

  • Online ISBN: 978-3-030-11286-8

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