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[LCd17] SGVCut: A Vertex-Cut Partitioning Tool for Random Walks-based Computations over Social Network graphs
Atelier, Poster ou Démonstration dans une Conférence Internationale : International Conference on Scientific and Statistical Database Management (SSDBM), June 2017, pp.1--4, Etats-Unis,
motcle:
Résumé:
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.
Collaboration:
LIP6
BibTeX
| @inproceedings { | |||
| LCd17, | |||
| title | = | "{SGVCut: A Vertex-Cut Partitioning Tool for Random Walks-based Computations over Social Network graphs}", | |
| author | = | " Y. Li and C. Constantin and C. du Mouza ", | |
| booktitle | = | "{International Conference on Scientific and Statistical Database Management (SSDBM)}", | |
| year | = | 2017, | |
| month | = | "June", | |
| pages | = | "1--4", | |
| address | = | " Etats-Unis", | |
| } | |||
