Qube a quick algorithm for updating betweenness centrality

This simplification approach deterministically filters (instead of clustering) the graph to retain important node and edge semantics, and works both automatically and interactively.The improved graph filtering and layout is combined with a novel computer graphics anisotropic shading of the dense crisscrossing array of edges to yield a full social network and scale-free graph visualization system.The scalability and raw performance of the example applications, using the combinatorial BLAS, are unprecedented on distributed memory clusters. We present a new lock-free parallel algorithm for computing betweenness centrality of massive complex networks that achieves better spatial locality compared with previous approaches.Betweenness centrality is a key kernel in analyzing the importance of vertices (or edges) in applications ranging fr ..." We present a new lock-free parallel algorithm for computing betweenness centrality of massive complex networks that achieves better spatial locality compared with previous approaches.

We describe the Parallel Combinatorial BLAS, which consists of a small but powerful set of linear algebra primitives specifically targeting graph and data mining applications.Large combinatorial graphs appear in many applications of high-performance computing, including computational biology, informatics, analytics, web search, dynamical systems, and sparse matrix methods.Graph computations are difficult to parallelize using traditional approaches due to their irregular nature and low operational intensity.We use Graph CT to analyze public data from Twitter, a microblogging network.Twitter’s message connections appear primarily tree-structured as a news dissemination system.

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