In our work, we use closeness centrality as a strategy in choosing central points ... This paper presents a scalable high-performance software library to be used for graph analysis and data mining.
Large combinatorial graphs appear in many applications of high-performance computing, including computational biology, informatics, analytics, web search, dynamical systems, and sparse mat ..." This paper presents a scalable high-performance software library to be used for graph analysis and data mining.
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.
This paper proposes novel methods for visualizing specifically the large power-law graphs that arise in sociology and the sciences.
In such cases a large portion of edges can be shown to be less important and removed while preserving component connectedness and other features (e.g.
Both quantitative analysis and visual results demonstrate the effectiveness of this approach. Social networks produce an enormous quantity of data.
Facebook consists of over 400 million active users sharing over 5 billion pieces of information each month.
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.
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.
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.