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