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Correlation and dimension relevance in multidimensional networks: a systematic taxonomy

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Abstract

Community discovery in networks is one of the most popular topics of modern network science. Given the spread of social networks extracted from applications data, it would be important to recognize that the study of communities in multidimensional networks is becoming a major issue since many individuals can maintain several types of relationships through these applications. The relevance of a dimension in a multidimensional network is an emerging issue that could affect communities’ detection and could be of interest for correlated multidimensional networks meaning those in which there is an equivalence relationship between nodes of different dimensions. The objective of this paper is twofold: (1) providing a study on the importance of relevant dimensions in community detection and (2) presenting an updated overview of some community detection methods in multidimensional networks in order to guide scholars and practitioners in their choices. Afterwards, we highlight some limits and identify features which are forsaken by existing approaches, in order to point up how we can deal with them. Finally, we provide further research directions as well as some open challenges.

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Authors and Affiliations

  1. Computer Science Department, University of Yaounde I, Yaoundé, Cameroon

    Félicité Gamgne Domgue, Norbert Tsopzé & René Ndoundam

  2. IRD, UMMISCO, Sorbonne University, 93143, Bondy, France

    Félicité Gamgne Domgue & Norbert Tsopzé

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  1. Félicité Gamgne Domgue
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  2. Norbert Tsopzé
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  3. René Ndoundam
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Correspondence to Félicité Gamgne Domgue.

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Gamgne Domgue, F., Tsopzé, N. & Ndoundam, R. Correlation and dimension relevance in multidimensional networks: a systematic taxonomy. Soc. Netw. Anal. Min. 11, 92 (2021). https://doi.org/10.1007/s13278-021-00801-8

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