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Bipartite-Oriented Distributed Graph Partitioning for Big Learning
2015
Journal of Computer Science and Technology
Many machine learning and data mining (MLDM) problems like recommendation, topic modeling and medical diagnosis can be modeled as computing on bipartite graphs. However, most distributed graph-parallel systems are oblivious to the unique characteristics in such graphs and existing online graph partitioning algorithms usually causes excessive replication of vertices as well as significant pressure on network communication. This article identifies the challenges and opportunities of partitioning
doi:10.1007/s11390-015-1501-x
fatcat:2oa5et2lurdbrfobslycy4k2em