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

Abstract : Several distributed frameworks have recently emerged to perform computations on large-scale graphs. However some recent studies have highlighted that vertex-partitioning approaches, e.g. Giraph, failed to achieve workload-balanced partitioning for skewed graphs, typically having a heavy- tail degree distribution. While edge-partitioning approaches such as PowerGraph and GraphX provide better balancing and performances for graph computation, they supply a generic framework, independent from the computation. This demonstration presents SGVCut to display our edge partitions designed for random walks-based computation, which is the foundation of many graph algorithms, on skewed graphs. The demonstration scenario introduces SGVCut interface and illustrates the benefits of our approach compare to other partitioning strategies for different settings and algorithms.
Type de document :
Communication dans un congrès
International Conference on Scientific and Statistical Database Management, SSDBM, Jun 2017, Chicago, United States. pp.39:1--39:4
Liste complète des métadonnées

http://hal.upmc.fr/hal-01515676
Contributeur : Camelia Constantin <>
Soumis le : jeudi 27 avril 2017 - 22:16:02
Dernière modification le : mercredi 21 mars 2018 - 18:58:10

Identifiants

  • HAL Id : hal-01515676, version 1

Collections

Citation

Yifan Li, Camelia Constantin, Cédric Du Mouza. SGVCut: A Vertex-Cut Partitioning Tool for Random Walks-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. 〈hal-01515676〉

Partager

Métriques

Consultations de la notice

254