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

Abstract

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 scalable partitioning algorithm is crucial for large-scale distributed graph mining.