The Johnson-Lindenstrauss Lemma and the sphericity of some graphs
Journal of Combinatorial Theory Series A
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
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Journal of the ACM (JACM)
An elementary proof of a theorem of Johnson and Lindenstrauss
Random Structures & Algorithms
Introduction to Probability Models, Ninth Edition
Introduction to Probability Models, Ninth Edition
A Centrality Measure for Electrical Networks
HICSS '08 Proceedings of the Proceedings of the 41st Annual Hawaii International Conference on System Sciences
Approximating betweenness centrality
WAW'07 Proceedings of the 5th international conference on Algorithms and models for the web-graph
Automatic skill acquisition in reinforcement learning using graph centrality measures
Intelligent Data Analysis
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There are several potent measures for mining the relationships among actors in social network analysis. Betweenness centrality measure is extensively utilized in network analysis. However, it is quite time-consuming to compute exactly the betweenness centrality in high dimensional social networks. Applying random projection approach, an approximation algorithm for computing betweenness centrality of a given node, is proposed in this paper, for both weighted and unweighted graphs. It is proved that the proposed method works better than the existing methods to approximate the betweenness centrality measure. The proposed algorithm significantly reduces the number of single-source shortest path computations. We test the method on real-world networks and a synthetic benchmark and observe that the proposed algorithm shows very promising results based on statistical evaluation measure.