Combining Weighted Centrality and Network Clustering

Bohn, Angela and Theußl, Stefan and Feinerer, Ingo and Hornik, Kurt ORCID: https://orcid.org/0000-0003-4198-9911 and Mair, Patrick and Walchhofer, Norbert (2009) Combining Weighted Centrality and Network Clustering. Research Report Series / Department of Statistics and Mathematics, 97. Department of Statistics and Mathematics, WU Vienna University of Economics and Business, Vienna.

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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.

Item Type: Paper
Keywords: social network analysis / centrality measure / closeness / clustering / R-help
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Repository Administrator
Date Deposited: 09 Dec 2009 19:13
Last Modified: 24 Oct 2019 13:41
URI: https://epub.wu.ac.at/id/eprint/1466

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