Scalable Edge Partitioning

Abstract

Edge-centric distributed computations have appeared as a recent technique to improve the shortcomings of think-like-a-vertex algorithms on large scale-free networks. In order to increase parallelism on this model, edge partitioning—partitioning edges into roughly equally sized blocks—has emerged as an alternative to traditional (node-based) graph partitioning. In this work, we develop a fast parallel split-and-connect graph construction algorithm in the distributed setting and show that combining our parallel construction with advanced parallel node partitioning algorithms yields high-quality edge partitions in a scalable way. Our technique scales to networks with billions of edges, and runs efficiently on thousands of PEs. Our extensive experiments show that our algorithm computes solutions of high quality on large real-world networks and large hyperbolic random graphs—which have a power law degree distribution and are therefore specifically targeted by edge partitioning.