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[CdL19] A Label-based Edge Partitioning for Multi-Layer Graphs
Conférence Internationale avec comité de lecture : Intl. Hawaii International Conference on System Sciences (HICSS), January 2019, pp.101-110, USA,Mots clés: Graph partitioning, real large graphs, multi-layer graphs
Résumé:
Social network systems rely on very large underlying
graphs. Consequently, to achieve scalability, most data
analytics and data mining algorithms are distributed
and graphs are partitioned over a set of servers. In
most real-world graphs, the edges and/or vertices have
different semantics and queries largely consider this
semantics. But while several works focus on efficient
graph computations on these “multi-semantic†graphs,
few ones are dedicated to their partitioning. In this
work, we propose a novel approach to achieve edge
partitioning for multi-layer graphs, which considers
both structural and edge-types (labels) localities. Our
experiments on real life datasets with benchmark
graph applications confirm that the execution time and
the inter-partition communication can be significantly
reduced with our approach.
Collaboration:
LIP6
BibTeX
| @inproceedings { | |||
| CdL19, | |||
| title | = | "{A Label-based Edge Partitioning for Multi-Layer Graphs}", | |
| author | = | " C. Constantin and C. du Mouza and Y. Li ", | |
| booktitle | = | "{Intl. Hawaii International Conference on System Sciences (HICSS)}", | |
| year | = | 2019, | |
| month | = | "January", | |
| pages | = | "101-110", | |
| address | = | " USA", | |
| } | |||
