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Combining Weighted Centrality and Network Clustering

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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. (author´s abstract) ; Series: Research Report Series / Department of Statistics and Mathematics

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Angela Bohn, Stefan Theußl, Ingo Feinerer, Kurt Hornik, Patrick Mair, Norbert Walchhofer

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