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Community Detection in Multi-relational Bibliographic Networks

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Book cover Database and Expert Systems Applications (DEXA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9828))

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Abstract

In this paper, we introduce a community detection approach from heterogeneous multi-relational network which incorporate the multiple types of objects and relationships, derived from a bibliographic networks. The proposed approach performs firstly by constructing the relation context family (RCF) to represent the different objects and relations in the multi-relational bibliographic networks using the Relational Concept Analysis (RCA) methods; and secondly by exploring such RCF for community detection. Experiments performed on a dataset of academic publications from the Computer Science domain enhance the effectiveness of our proposal and open promising issues.

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Acknowledgements

This work is partially supported by the French-Tunisian project PHC-Utique RIMS-FD 14G 1404.

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Authors and Affiliations

  1. LIPAH, Faculty of Sciences of Tunis, Université de Tunis El Manar, Tunis, Tunisia

    Soumaya Guesmi, Chiraz Trabelsi & Chiraz Latiri

Authors
  1. Soumaya Guesmi
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  2. Chiraz Trabelsi
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  3. Chiraz Latiri
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Corresponding author

Correspondence to Soumaya Guesmi .

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Editors and Affiliations

  1. Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Sven Hartmann

  2. Victoria University of Wellington, Wellington, New Zealand

    Hui Ma

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Guesmi, S., Trabelsi, C., Latiri, C. (2016). Community Detection in Multi-relational Bibliographic Networks. In: Hartmann, S., Ma, H. (eds) Database and Expert Systems Applications. DEXA 2016. Lecture Notes in Computer Science(), vol 9828. Springer, Cham. https://doi.org/10.1007/978-3-319-44406-2_2

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  • DOI: https://doi.org/10.1007/978-3-319-44406-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44405-5

  • Online ISBN: 978-3-319-44406-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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