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Towards Causal Explanations of Community Detection in Networks

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Complex Networks & Their Applications IX (COMPLEX NETWORKS 2020 2020)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 943))

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Abstract

Community detection is a significant research problem in Network Science since it identifies groups of nodes that may have certain functional importance - termed communities. Our goal is to further study this problem from a different perspective related to the questions of the cause of belongingness to a community. To this end, we apply the framework of causality and responsibility developed by Halpern and Pearl [11]. We provide an algorithm-semi-agnostic framework for computing causes and responsibility of belongingness to a community. To the best of the authors’ knowledge, this is the first work that examines causality in community detection. Furthermore, the proposed framework is easily adaptable to be also used in other network processing operations apart from community detection.

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Notes

  1. 1.

    The endogenous and exogenous sets differ for different nodes. Also, the endogenous set typically comprises the incident edges connecting this node to its neighbors. Finally, allowing self-loops is not an issue, since if they are irrelevant to the setting, we can simply make them exogenous.

  2. 2.

    Transitivity does not hold in general w.r.t. causation [11].

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Acknowledgements

Georgia Baltsou is co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Programme “Human Resources Development, Education and Lifelong Learning” in the context of the project “Strengthening Human Resources Research Potential via Doctorate Research - 2nd Cycle” (MIS-5000432), implemented by the State Scholarships Foundation (IKY).

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Authors and Affiliations

  1. School of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Georgia Baltsou, Anastasios Gounaris & Apostolos N. Papadopoulos

  2. Department of Computer Engineering and Informatics, University of Patras, Patras, Greece

    Konstantinos Tsichlas

Authors
  1. Georgia Baltsou
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  2. Anastasios Gounaris
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  3. Apostolos N. Papadopoulos
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  4. Konstantinos Tsichlas
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Corresponding author

Correspondence to Georgia Baltsou .

Editor information

Editors and Affiliations

  1. Grupo de Sistemas Complejos, Universidad Politécnica de Madrid, Madrid, Madrid, Spain

    Rosa M. Benito

  2. IUT Lumière, University of Lyon, Bron Cedex, France

    Chantal Cherifi

  3. LIB, UFR Sciences et Techniques, Université de Bourgogne, Dijon, France

    Hocine Cherifi

  4. Grupo Interdisciplinar de Sistemas Complejos, Departamento de Matematicas, Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Center for Social and Biomedical Complexity, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Luis Mateus Rocha

  6. Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Tarragona, Spain

    Marta Sales-Pardo

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Baltsou, G., Gounaris, A., Papadopoulos, A.N., Tsichlas, K. (2021). Towards Causal Explanations of Community Detection in Networks. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_14

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  • DOI: https://doi.org/10.1007/978-3-030-65347-7_14

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