Distributed Direction-Optimizing Label Propagation for Community Detection

Abstract

Designing a scalable algorithm for community detection is challenging due to the simultaneous need for both high performance and quality of solution. We propose a new distributed algorithm for community detection based on a novel Label Propagation algorithm. The algorithm is inspired by the direction optimization technique in graph traversal algorithms, relies on the use of frontiers, and alternates between abstractions called label push and label pull. This organization creates flexibility and affords us with opportunities for balancing performance and quality of solution. We implement our algorithm in distributed memory with the active-message based asynchronous many-task runtime AM++. We experiment with two seeding strategies for the initial seeding stage, namely, random seeding and degree seeding. With the Graph Challenge dataset, our distributed implementation, in conjunction with the runtime support, detects the communities in graphs having 20 million vertices in less than one second while achieving reasonably high quality of solution.