A Label-based Edge Partitioning for Multi-Layer Graphs
- Camelia Constantin, LIP6, Sorbonne Université , Paris, France
- Cedric du Mouza, CEDRIC, CNAM, Paris, France
- Yifan Li, LIP6, Sorbonne Université, Paris, France
AbstractSocial 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.
Return to previous page