TY - UNPB
T1 - Combining Weighted Centrality and Network Clustering
AU - Bohn, Angela
AU - Theußl, Stefan
AU - Feinerer, Ingo
AU - Hornik, Kurt
AU - Mair, Patrick
AU - Walchhofer, Norbert
PY - 2009/2/1
Y1 - 2009/2/1
N2 - 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.
AB - 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.
UR - http://statmath.wu.ac.at/
M3 - WU Working Paper
T3 - Research Report Series / Department of Statistics and Mathematics
BT - Combining Weighted Centrality and Network Clustering
ER -