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Partitioning Graphs for the Cloud using Reinforcement Learning
[article]
2019
arXiv
pre-print
In this paper, we propose Revolver, a parallel graph partitioning algorithm capable of partitioning large-scale graphs on a single shared-memory machine. Revolver employs an asynchronous processing framework, which leverages reinforcement learning and label propagation to adaptively partition a graph. In addition, it adopts a vertex-centric view of the graph where each vertex is assigned an autonomous agent responsible for selecting a suitable partition for it, distributing thereby the
arXiv:1907.06768v2
fatcat:u666xe5n7rfppaytiicqpxz3wq