Performance of linear-space search algorithms
Artificial Intelligence
On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Identifying sets of key players in a social network
Computational & Mathematical Organization Theory
Incremental deployment of network monitors based on Group Betweenness Centrality
Information Processing Letters
Routing betweenness centrality
Journal of the ACM (JACM)
Maximum betweenness centrality: approximability and tractable cases
WALCOM'11 Proceedings of the 5th international conference on WALCOM: algorithms and computation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Effective co-betweenness centrality computation
Proceedings of the 7th ACM international conference on Web search and data mining
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In many applications we are required to locate the most prominent group of vertices in a complex network. Group Betweenness Centrality can be used to evaluate the prominence of a group of vertices. Evaluating the Betweenness of every possible group in order to find the most prominent is not computationally feasible for large networks. In this paper we present two algorithms for finding the most prominent group. The first algorithm is based on heuristic search and the second is based on iterative greedy choice of vertices. The algorithms were evaluated on random and scale-free networks. Empirical evaluation suggests that the greedy algorithm results were negligibly below the optimal result. In addition, both algorithms performed better on scale-free networks: heuristic search was faster and the greedy algorithm produced more accurate results. The greedy algorithm was applied for optimizing deployment of intrusion detection devices on network service provider infrastructure.