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Multidimensional networks: A novel node centrality metric based on common neighborhood

Abstract : Complex networks have been receiving increasing attention by the scientific community. They can be represented by multidimensional networks in which there is multiple types of connections between nodes. Thanks also to the increasing availability of analytical measures that have been extended in order to describe and analyze properties of entities involved in this kind of multiple relationship representation of networks. These measures focused on quantitative involvement of node through the widely popular and intuitive measure of degree. However, one aspect of such properties have been disregarded so far: entities in such networks are often tied according to the interest they have to their neighbors in the overall dimensions. In this paper, the problem of characterizing multidi-mensional networks, using a qualitative aspect of the node neighborhood, has been studied, through the new defined node centrality measure, Stability, to describe the connectivity of nodes that incorporates across-dimension topological features in order to identify the relevant dimensions. We assessed our measure on two real-world multidimensional networks, showing its validity, its meaningfulness and its correlation with a dimension connectivity measure.
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https://hal.archives-ouvertes.fr/hal-02934749
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Submitted on : Wednesday, September 9, 2020 - 4:00:50 PM
Last modification on : Tuesday, December 7, 2021 - 5:50:03 PM
Long-term archiving on: : Thursday, December 3, 2020 - 1:13:25 AM

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  • HAL Id : hal-02934749, version 1

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Félicité Gamgne Domgue, Norbert Tsopze, René Ndoundam. Multidimensional networks: A novel node centrality metric based on common neighborhood. CARI 2020 - Colloque Africain sur la Recherche en Informatique et en Mathématiques Appliquées, Oct 2020, Thiès, Senegal. ⟨hal-02934749⟩

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