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Combining Weighted Centrality and Network Clustering

2024/01/18 by Bohn, Angela, Theußl, Stefan, Feinerer, Ingo +3

paper · doi:10.57938/c161ad05-f53e-4386-b4c9-bbb4602bd4b3

Abstract

In Social Network Analysis (SNA) centrality measures focus on activity (degree), information access (betweenness), distance to all the nodes (closeness), or popularity (pagerank). We introduce a new measure quantifying the distance of nodes to the network center. It is called weighted distance to nearest center (WDNC) and it is based on edge-weighted closeness (EWC), a weighted version of closeness. It combines elements of weighted centrality as well as clustering. The WDNC will be tested on two e-mail networks of the R community, one of the most important open source programs for statistical computing and graphics. We will find that there is a relationship between the WDNC and the formal organization of the R community.

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