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A parallel graph partitioning algorithm to speed up the large-scale distributed graph mining

2012
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Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining Algorithms, Systems, Programming Models and Applications - BigMine '12
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For the large-scale distributed graph mining, the graph is distributed over a cluster of nodes, thus performing computations on the distributed graph is expensive when large amount of data have to be moved between different computers. A good partitioning of distributed graph is needed to reduce the communication between computers and scale a system up. Existing graph partitioning algorithms incur high computation and communication cost when applied on large distributed graphs. A efficient and

doi:10.1145/2351316.2351325
dblp:conf/kdd/ZengWW12
fatcat:njhkucpffnc4jikyewnmxezysm