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We present Graph CT, a Graph Characterization Toolkit for massive graphs representing social network data.
On a 128processor Cray XMT, Graph CT estimates the betweenness centrality of an artificially generated (R-MAT) 537 million vertex, 8.6 billion edge graph in 55 minutes and a realworld graph (Kwak, et al.) with 61.6 million vertices and 1.47 billion edges in 105 minutes.
KDT provides a flexible Py ..." The Knowledge Discovery Toolbox (KDT) enables domain experts to perform complex analyses of huge datasets on supercomputers using a high-level language without grappling with the difficulties of writing parallel code, calling parallel libraries, or becoming a graph expert.
KDT provides a flexible Python interface to a small set of high-level graph operations; composing a few of these operations is often sufficient for a specific analysis.
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