A tensor-based unified approach for clustering coefficients in financial multiplex networks

Bartesaghi, Paolo; Clemente, Gian Paolo; Grassi, Rosanna

doi:10.1016/j.ins.2022.04.021

Cited by 13 publications

(1 citation statement)

References 56 publications

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“…This article quantifies the importance of nodes from the perspective of fusion. Fusing the degree, betweenness (Aldabobi & Jabri, 2019), clustering coefficient (Bartesaghi & Grassi, 2022) and influence of the node through Entropy method to form a comprehensive index Node Domain of the node, which is utilized to improve the selection of seed nodes in the initial community. F I G U R E 3 The similarity of interest preference.…”

Section: Improved Methods Of Seed Nodes' Selectionmentioning

confidence: 99%

An effective overlapping community merging method oriented to multidimensional attribute social networks

Cheng,

Yang,

Cheng

et al. 2023

Expert Systems

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For multidimensional attribute social networks, most of the existing methods have limitations in dealing with community redundancy, namely, the influences of the network's multidimensional attributes and users' interest preferences are ignored in the process of community merging. To this end, we propose an effective overlapping community merging method (EOCMM) oriented to multidimensional attribute social networks. In EOCMM, we focus on two core problems which are how to improve the method of improving overlapping community detection in multidimensional attribute social networks and how to merge the detected communities with high redundancy degree. To solve the first problem, based on the network topology characteristics and user interest preference, the Node Domain index is proposed to improve and optimize the selection of seed nodes. The traditional overlapping community detection method is improved by semantic fitness function and seed nodes. To solve the second problem, we merge the communities with high redundancy degree by Semantic Community Overlapping Degree which is fused by user similarity, community similarity and community tightness. Finally, we compared our method with other multiple mainstream methods by extensive experiments on three real social network datasets. The experimental comparison results show that the improvements on three evaluation metrics extension of modularity, partition density and semantic extension of modularity are 6.04%, 7.96% and 3.13%, respectively. And our method can make the results of community detection and merging more reasonable and effective.

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Section: Improved Methods Of Seed Nodes' Selectionmentioning

confidence: 99%