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Identifying, Ranking and Tracking Community Leaders in Evolving Social Networks

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Complex Networks and Their Applications VIII (COMPLEX NETWORKS 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 881))

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Abstract

Discovering communities in a network is a fundamental and important problem to complex networks. Find the most influential actors among its peers is a major task. If on one side, studies on community detection ignore the influence of actors and communities, on the other hand, ignoring the hierarchy and community structure of the network neglect the actor or community influence. We bridge this gap by combining a dynamic community detection method with a dynamic centrality measure. The proposed enhanced dynamic hierarchical community detection method computes centrality for nodes and aggregated communities and selects each community representative leader using the ranked centrality of every node belonging to the community. This method is then able to unveil, track, and measure the importance of main actors, network intra and inter-community structural hierarchies based on a centrality measure. The empirical analysis performed, using two temporal networks shown that the method is able to find and tracking community leaders in evolving networks.

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Notes

  1. 1.

    Figure 1 online here: https://mmfcordeiro.github.io/LaplaceLouvainResults/Intro.html.

  2. 2.

    Figure 2 online here: https://mmfcordeiro.github.io/LaplaceLouvainResults/Intro2.html.

  3. 3.

    Figure 6 online here: https://mmfcordeiro.github.io/LaplaceLouvainResults/Karate.html.

  4. 4.

    Figure 7 online here: https://mmfcordeiro.github.io/LaplaceLouvainResults/Jure.html.

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Author information

Authors and Affiliations

  1. Faculty of Engineering, University of Porto, Porto, Portugal

    Mário Cordeiro

  2. Laboratory of Artificial Intelligence and Decision Support, Porto, Portugal

    Mário Cordeiro, Rui Portocarrero Sarmento, Pavel Brazdil & João Gama

  3. Faculty of Science and Technology, Ryukoku University, Kyoto, Japan

    Masahiro Kimura

Authors
  1. Mário Cordeiro
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  2. Rui Portocarrero Sarmento
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  3. Pavel Brazdil
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  4. Masahiro Kimura
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  5. João Gama
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Corresponding author

Correspondence to Mário Cordeiro .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. Università degli Studi di Milano, Milan, Italy

    Sabrina Gaito

  3. University of Aveiro, Aveiro, Portugal

    José Fernendo Mendes

  4. Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Indiana University, Bloomington, IN, USA

    Luis Mateus Rocha

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Cordeiro, M., Sarmento, R.P., Brazdil, P., Kimura, M., Gama, J. (2020). Identifying, Ranking and Tracking Community Leaders in Evolving Social Networks. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_17

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