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Node-Centric Detection of Overlapping Communities in Social Networks

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3rd International Winter School and Conference on Network Science (NetSci-X 2017)

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

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Abstract

We present NECTAR, a community detection algorithm that generalizes Louvain method’s local search heuristic for overlapping community structures. NECTAR chooses dynamically which objective function to optimize based on the network on which it is invoked. Our experimental evaluation on both synthetic benchmark graphs and real-world networks, based on ground-truth communities, shows that NECTAR provides excellent results as compared with state of the art community detection algorithms.

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Notes

  1. 1.

    NECTAR code and documentation may be downloaded from: https://github.com/amirubin87/NECTAR.

  2. 2.

    If no gain is positive, v remains as a singleton.

  3. 3.

    For more details on parameter values used for LFR, refer to [21].

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Acknowledgements

Partially supported by the Cyber Security Research Center at Ben-Gurion University and by the Lynne and William Frankel Center for Computer Science.

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Authors and Affiliations

  1. Computer Science Department, Ben-Gurion University of the Negev, Be’er-Sheva, Israel

    Yehonatan Cohen, Danny Hendler & Amir Rubin

Authors
  1. Yehonatan Cohen
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  2. Danny Hendler
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  3. Amir Rubin
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Corresponding author

Correspondence to Yehonatan Cohen .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Tel Aviv University, Tel Aviv, Israel

    Erez Shmueli

  2. Department of Mathematics, Bar-Ilan University, Ramat-Gan, Israel

    Baruch Barzel

  3. Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Rami Puzis

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Cohen, Y., Hendler, D., Rubin, A. (2017). Node-Centric Detection of Overlapping Communities in Social Networks. In: Shmueli, E., Barzel, B., Puzis, R. (eds) 3rd International Winter School and Conference on Network Science . NetSci-X 2017. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-55471-6_1

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