SGVCut: A Vertex-Cut Partitioning Tool for RandomWalks-based Computations over Social Network graphs - Sorbonne Université Access content directly
Conference Papers Year : 2017

SGVCut: A Vertex-Cut Partitioning Tool for RandomWalks-based Computations over Social Network graphs

Yifan Li
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Camelia Constantin

Abstract

Several distributed frameworks have recently emerged to perform computations on large-scale graphs. However somerecent studies have highlighted that vertex-partitioning approaches, e.g. Giraph, failed to achieve workload-balancedpartitioning for skewed graphs, typically having a heavy-tail degree distribution. While edge-partitioning approachessuch as PowerGraph and GraphX provide better balancing and performances for graph computation, they supply ageneric framework, independent from the computation. Thisdemonstration presents SGVCut to display our edge partitions designed for random walks-based computation, whichis the foundation of many graph algorithms, on skewed graphs.The demonstration scenario introduces SGVCut interface andillustrates the benefits of our approach compare to other partitioning strategies for different settings and algorithms.
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Dates and versions

hal-01515676 , version 1 (27-04-2017)

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Yifan Li, Camelia Constantin, Cédric Du Mouza. SGVCut: A Vertex-Cut Partitioning Tool for RandomWalks-based Computations over Social Network graphs. International Conference on Scientific and Statistical Database Management, SSDBM, Jun 2017, Chicago, United States. pp.39:1--39:4, ⟨10.1145/3085504.3091114⟩. ⟨hal-01515676⟩
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