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
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;
Conference_Titel :
Signal-Image Technology and Internet-Based Systems (SITIS), 2014 Tenth International Conference on
DOI :
10.1109/SITIS.2014.15
