Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
The DBLP Computer Science Bibliography: Evolution, Research Issues, Perspectives
SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Identifying sets of key players in a social network
Computational & Mathematical Organization Theory
Balancing Systematic and Flexible Exploration of Social Networks
IEEE Transactions on Visualization and Computer Graphics
Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Finding the most prominent group in complex networks
AI Communications - Network Analysis in Natural Sciences and Engineering
Bowling Alone and Trust Decline in Social Network Sites
DASC '09 Proceedings of the 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing
Routing betweenness centrality
Journal of the ACM (JACM)
Traffic density-based discovery of hot routes in road networks
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
Journal of Network and Computer Applications
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Betweenness centrality of vertices is essential in the analysis of social and information networks, and co-betweenness centrality is one of two natural ways to extend it to sets of vertices. Existing algorithms for co-betweenness centrality computation suffer from at least one of the following problems: i) their applicability is limited to special cases like sequences, sets of size two, and ii) they are not efficient in terms of time complexity. In this paper, we present efficient algorithms for co-betweenness centrality computation of any set or sequence of vertices in weighted and unweighted networks. We also develop effective methods for co-betweenness centrality computation of sets and sequences of edges. These results provide a clear and extensive view about the complexity of co-betweenness centrality computation for vertices and edges in weighted and un-weighted networks. Finally, we perform extensive experiments on real-world networks from different domains including social, information and communication networks, to show the empirical efficiency of the proposed methods.