Identifying Influential Nodes in Bipartite Networks Using the Clustering Coefficient

被引：8

作者：

Liebig, Jessica ^{[1
]}

Rao, Asha ^{[1
]}

机构：

[1] RMIT Univ, Sch Math & Geospatial Sci, Melbourne, Vic, Australia

来源：

10TH INTERNATIONAL CONFERENCE ON SIGNAL-IMAGE TECHNOLOGY AND INTERNET-BASED SYSTEMS SITIS 2014 | 2014年

关键词：

COMPLEX NETWORKS; COMMUNITY STRUCTURE;

D O I：

10.1109/SITIS.2014.15

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：323 / 330

页数：8

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