Temporal Degree-Degree and Closeness-Closeness: A New Centrality Metrics for Social Network Analysis

Mathematics

Mahmoud Elmezain¹  Hani M. Ibrahim²  Ebtesam A. Othman³ 

[1] Computer Science Division, Faculty of Science, Tanta University, Tanta 31527, Egypt;Faculty of Science and Computer Engineering, Taibah University, Yanbu 966144, Saudi Arabia;Mathematics & Computer Science Department, Faculty of Science, Menoufiya University, Menoufia 32511, Egypt;

关键词: social network analysis;    time-ordered weighted graph;    centrality measures;    temporal degree centrality;    temporal closeness centrality;    temporal degree-degree;   

DOI  :  10.3390/math9222850

来源: DOAJ

【 摘 要 】

In the area of network analysis, centrality metrics play an important role in defining the “most important” actors in a social network. However, nowadays, most types of networks are dynamic, meaning their topology changes over time. The connection weights and the strengths of social links between nodes are an important concept in a social network. The new centrality measures are proposed for weighted networks, which relies on a time-ordered weighted graph model, generalized temporal degree and closeness centrality. Furthermore, two measures—Temporal Degree-Degree and Temporal Closeness-Closeness—are employed to better understand the significance of nodes in weighted dynamic networks. Our study is caried out according to real dynamic weighted networks dataset of a university-based karate club. Through extensive experiments and discussions of the proposed metrics, our analysis proves that there is an effectiveness on the impact of each node throughout social networks.

【 授权许可】

Unknown