U, Kang; Charalampos, Tsourakakis; Christos, Faloutsos
PEGASUS: mining peta-scale graphs
In this paper, we describe P e G a S us , an open source Peta Graph Mining library which performs typical graph mining tasks such as computing the diameter of the graph, computing the radius of each node, finding the connected components, and computing the importance score of nodes. As the size of graphs reaches several Giga-, Tera- or Peta-bytes, the necessity for such a library grows too. To the best of our knowledge, P e G a S us is the first such library, implemented on the top of the H adoop platform, the open source version of M ap R educe . Many graph mining operations (PageRank, spectral clustering, diameter estimation, connected components, etc.) are essentially a repeated matrix-vector multiplication. In this paper, we describe a very important primitive for P e G a S us , called GIM-V (generalized iterated matrix-vector multiplication). GIM-V is highly optimized, achieving (a) good scale-up on the number of available machines, (b) linear running time on the number of edges, and (c) more than 5 times faster performance over the non-optimized version of GIM-V . Our experiments ran on M45, one of the top 50 supercomputers in the world. We report our findings on several real graphs, including one of the largest publicly available Web graphs, thanks to Yahoo!, with ≈ 6.7 billion edges.
http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.pngKnowledge and Information SystemsSpringer Journalshttp://www.deepdyve.com/lp/springer-journals/pegasus-mining-peta-scale-graphs-rWEqUdsRlc