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A Label-based Edge Partitioning for Multi-Layer Graphs

Camelia Constantin
Yifan Li
  • Function : Author

Abstract

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.
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Dates and versions

hal-02465807 , version 1 (04-02-2020)

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Camelia Constantin, Cédric Du Mouza, Yifan Li. A Label-based Edge Partitioning for Multi-Layer Graphs. 52nd Hawaii International Conference on System Sciences (HICSS 2019), Jan 2019, Maui, Hawaii, United States. pp.2216-2225, ⟨10.24251/HICSS.2019.269⟩. ⟨hal-02465807⟩
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