Abstract
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.
Highlights
While many social networks are dynamic and weighted, this paper explains the meaning of temporal degree, temporal closeness and temporal degree-degree centrality for such networks, there is a proposal for a new centrality measure to have a better approach understanding of the significance of nodes in weighted dynamic networks
While many social networks are dynamic, this research uses a time-ordered weighted graph model to suggest a generalized definition of temporal degree and temporal closeness for weighted networks
A new hybrid centrality measure based on closeness centrality, Temporal
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. While various static centrality metrics have been established and are widely utilized for SNA, dynamic measures are a relatively new research area. It is simple and common to examine static network snapshots independently and use the average attributes of all snapshots, for example, a feasible technique to quantify the topological significance of a node over time is to use the average value of all static snapshots on the node’s centrality Such dynamic analysis is limited, because temporal pathways that traverse many temporal snapshots are omitted. Kim and Anderson [23] enhanced the Tang idea by introducing a new model termed a time-ordered graph that may convert a dynamic network to a static network with directed flows. While many social networks are dynamic and weighted, this paper explains the meaning of temporal degree, temporal closeness and temporal degree-degree centrality for such networks, there is a proposal for a new centrality measure (temporary Closeness-closeness centrality) to have a better approach understanding of the significance of nodes in weighted dynamic networks
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