Qube a quick algorithm for updating betweenness centrality

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Nov,2003 [9] Filippo Radicchi, Claudio Castellano, Federico Cecconi, Vittorio Loreto, and Domenico Parisi. Statistical Properties of Community Structure in Large Social and Information Networks. Identifying Community Structures from Network Data via Maximum Likelihood Methods July,2009 [14] Mason A. Community detection algorithms: A comparative analysis 2010 [16] G. Communities in Networks 2009 [15] Andrea Lancichinetti, Santo Fortunato. Since a social network graph is frequently updated, it is necessary to update the betweenness centrality efficiently.When a graph is changed, the betweenness centralities of all the vertices should be recomputed from scratch using all the vertices in the graph.Besides that, recent years have seen the publication of dynamic algorithms for efficient recomputation of betweenness in evolving networks.In previous work we proposed the first semi-dynamic algorithms that recompute an of betweenness in connected graphs after batches of edge insertions. Identifying communities in Social Networks: A Survey 2005 [11] Chayant Tantipathananandh, Tanya Berger-Wolf, David Kempe.

In this paper, we study the update problem of betweenness centrality in fully dynamic graphs.

Self-Organization and Identification of Web Communities 2002 [7] M.

Finding and evaluating community structure in networks Aug,2003 [8] M. A Framework For Community Identification in Dynamic Social Networks 2007 [12] J.

In addition, we extend our former algorithm for semi-dynamic BFS to batches of both edge insertions and deletions.

Using approximation, our algorithms are the first to make in-memory computation of betweenness in fully-dynamic networks with millions of edges feasible.

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