Shared-Memory Parallel Maximal Clique Enumeration

TL;DR

This paper introduces scalable shared-memory parallel algorithms for Maximal Clique Enumeration, significantly improving speed and efficiency for large graph analysis by ensuring work-efficiency and low parallel depth.

Contribution

The work presents the first scalable shared-memory parallel algorithms for MCE that are provably work-efficient and have low parallel depth, outperforming prior methods.

Findings

Algorithms are work-efficient relative to sequential methods.

Algorithms demonstrate good speedup and scaling on multicore machines.

Implementation outperforms prior shared-memory parallel algorithms for MCE.

Abstract

We present shared-memory parallel methods for Maximal Clique Enumeration (MCE) from a graph. MCE is a fundamental and well-studied graph analytics task, and is a widely used primitive for identifying dense structures in a graph. Due to its computationally intensive nature, parallel methods are imperative for dealing with large graphs. However, surprisingly, there do not yet exist scalable and parallel methods for MCE on a shared-memory parallel machine. In this work, we present efficient shared-memory parallel algorithms for MCE, with the following properties: (1) the parallel algorithms are provably work-efficient relative to a state-of-the-art sequential algorithm (2) the algorithms have a provably small parallel depth, showing that they can scale to a large number of processors, and (3) our implementations on a multicore machine shows a good speedup and scaling behavior with increasing number of cores, and are substantially faster than prior shared-memory parallel algorithms for MCE.