DocumentCode
3585330
Title
Identifying Influential Nodes in Bipartite Networks Using the Clustering Coefficient
Author
Liebig, Jessica ; Rao, Asha
Author_Institution
Sch. of Math. & Geospatial Sci., RMIT Univ., Melbourne, VIC, Australia
fYear
2014
Firstpage
323
Lastpage
330
Abstract
The identification of influential nodes in complex network can be very challenging. If the network has a community structure, centrality measures may fail to identify the complete set of influential nodes, as the hubs and other central nodes of the network may lie inside only one community. Here we define a bipartite clustering coefficient that, by taking differently structured clusters into account, can find important nodes across communities.
Keywords
complex networks; network theory (graphs); pattern clustering; bipartite clustering coefficient; bipartite network; centrality measure; community structure; complex network; influential node; Collaboration; Communities; Conferences; Geospatial analysis; Joining processes; Length measurement; Loss measurement;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal-Image Technology and Internet-Based Systems (SITIS), 2014 Tenth International Conference on
Type
conf
DOI
10.1109/SITIS.2014.15
Filename
7081566
Link To Document