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A new algorithm for communities detection in social networks with node attributes

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Abstract

Revealing the community structure in social networks witnessed a determined effort. In this respect, a different category of social network can be handled, such as, dynamic social networks, social networks with node attributes, etc. In this article, we introduce a new method to solve this thriving issue in the social network with node attributes. This latter can be represented by a bipartite graph, which consists of a two sets of nodes and edges connecting these nodes. The tendency of people with similar node attributes leads to the hidden information of clusters or communities. A wealthy number of community-detection algorithms have been proposed for bipartite graphs and applied to several domains in the literature. To palliate some of the highlighted shortcomings, we introduce a new approach, called Fast-Bi Community Detection (FBCD), that aims to an efficient community detection in social networks. The main idea of this approach is to explore the set of maximum matching in the bipartite graph in order to reduce the complexity of our algorithm. The carried out experiments show the high quality of the obtained communities versus those by the pioneering ones of the literature.

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Notes

  1. \(\textit{Supp}(\wedge \textit{I}) = \cap {\phi (i), i \in \textit{I}}\).

  2. \(\textit{Supp}(\vee \textit{I}) = \cup {\phi (i), i \in \textit{I}}\).

  3. http://konect.uni-koblenz.de/.

  4. Southern women network dataset-KONECT (2016).

  5. American Revolution network dataset-KONECT (2016).

  6. Corporate Leadership network dataset-KONECT (2016).

  7. South African Companies network dataset-KONECT (2016).

  8. http://snap.stanford.edu.

  9. http://networkrepository.com.

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Acknowledgements

This work has been funded by the Justice Program of the European Union (2014-2020) 723180—RiskTrack—JUST-2015-JCOO-AG/JUST-2015-JCOO-AG-1. The contents of this publication are the sole responsibility of their authors and can in no way be taken to reflect the views of the European Commission.

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Authors and Affiliations

  1. Faculty of Sciences of Tunis, LIPAH-LR 11ES14, University of Tunis El Manar, 2092, Tunis, Tunisia

    Haifa Gmati & Amira Mouakher

  2. Escuela Politecnica Superior, Universidad Autonoma de Madrid, Madrid, Spain

    Antonio Gonzalez-Pardo & David Camacho

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  1. Haifa Gmati
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  2. Amira Mouakher
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  3. Antonio Gonzalez-Pardo
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  4. David Camacho
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Correspondence to Antonio Gonzalez-Pardo.

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Gmati, H., Mouakher, A., Gonzalez-Pardo, A. et al. A new algorithm for communities detection in social networks with node attributes. J Ambient Intell Human Comput 15, 1779–1791 (2024). https://doi.org/10.1007/s12652-018-1108-5

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