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Survey on Social Ego-Community Detection

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Complex Networks and Their Applications VII (COMPLEX NETWORKS 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 813))

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Abstract

Community detection is one of the main topics of social network analysis, which is attracting increasing attention from many researchers. In fact, the community detection can be done either from the global network (such is the case of global communities), or from some specific nodes: case of ego-communities. The early community detection works focused on network partitioning into several global communities. Over time, researchers have been interested in studying ego-communities to analyze the impact of interest nodes within network. Even if the global community survey is very well covered through works like that of Fortunato; that relating to ego-communities is not yet. The purpose of this paper is to propose a survey on ego-community detection approaches in order to reduce the survey lack while focusing on the strengths and weaknesses of existing solutions.

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Notes

  1. 1.

    The interest node is also called ego.

  2. 2.

    A graph is simple if it contains neither loops nor multiple edges.

  3. 3.

    These cliques are called \(\alpha \)-Optimal-Quasi-Cliques (\(\alpha \)-OQC).

  4. 4.

    An algorithm is said to be deterministic if it still detects the same communities by running it multiple times on the same dataset.

  5. 5.

    An algorithm is stable if it does not find strongly different communities on two topologically similar graphs.

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Authors and Affiliations

  1. Cheikh Anta Diop University, Dakar - Fann BP, 5005, Senegal

    Ahmed Ould Mohamed Moctar & Idrissa Sarr

Authors
  1. Ahmed Ould Mohamed Moctar
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  2. Idrissa Sarr
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Corresponding author

Correspondence to Ahmed Ould Mohamed Moctar .

Editor information

Editors and Affiliations

  1. Nokia Bell Labs, Cambridge, UK

    Luca Maria Aiello

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

    Chantal Cherifi

  3. LE2I UMR CNRS 6306 9, University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  4. Mathematical Institute, University of Oxford, Oxford, UK

    Renaud Lambiotte

  5. Department of Computer Science and Technology, The Computer Laboratory, University of Cambridge, Cambridge, UK

    Pietro Lió

  6. Center for Complex Networks and Systems Research, School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Luis M. Rocha

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Ould Mohamed Moctar, A., Sarr, I. (2019). Survey on Social Ego-Community Detection. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 813. Springer, Cham. https://doi.org/10.1007/978-3-030-05414-4_31

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