Clustering networks' heterogeneous data in defining a comprehensive closeness centrality index
type: Item
instance of: scholarly article
title: Clustering networks' heterogeneous data in defining a comprehensive closeness centrality index
zbMATH Open document ID: 1407.91197
DOI: 10.1155/2014/202350
author: Farnaz Barzinpour
author: B. Hoda Ali-Ahmadi
author: Somayeh Alizadeh
author: S. Golamreza Jalali Naini
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: One of the most important applications of network analysis is detecting community structure, or clustering. Nearly all algorithms that are used to identify these structures use information derived from the topology of these networks, such as adjacency and distance relationships, and assume that there is only one type of relation in the network. However, in reality, there are multilayer networks, with each layer representing a particular type of relationship that contains nodes with individual characteristics that may influence the behavior of networks. This paper introduces a new, efficient spectral approach for detecting the communities in multilayer networks using the concept of hybrid clustering, which integrates multiple data sources, particularly the structure of relations and individual characteristics of nodes in a network, to improve the comprehension of the network and the clustering accuracy. Furthermore, we develop a new algorithm to define the closeness centrality measure in complex networks based on a combination of two approaches: social network analysis and traditional social science approach. We evaluate the performance of our proposed method using four benchmark datasets and a real-world network: oil global trade network. The experimental results indicated that our hybrid method is sufficiently effective at clustering using the node attributes and network structure.
Mathematics Subject Classification ID: 91D30
Mathematics Subject Classification ID: 05C82
zbMATH DE Number: 7015897
Wikidata QID: Q59063737
describes a project that uses: GenLouvain
describes a project that uses: SA-cluster
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/202350
OpenAlex ID: W2078296583
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description: scientific article; zbMATH DE number 7015897