Parallel Correlation Clustering on Big Graphs

TL;DR

This paper introduces two parallel algorithms, C4 and ClusterWild!, for correlation clustering on large graphs, achieving near-linear speedups and high accuracy, significantly reducing runtime compared to existing methods.

Contribution

The paper presents novel parallel algorithms for correlation clustering that run in polylogarithmic rounds, with provable guarantees and superior scalability on large graphs.

Findings

C4 guarantees a 3-approximation ratio with serializability.

ClusterWild! offers a trade-off with a small loss in approximation for better scalability.

Both algorithms outperform state-of-the-art methods in accuracy and speed, clustering billion-edge graphs in under 5 seconds.

Abstract

Given a similarity graph between items, correlation clustering (CC) groups similar items together and dissimilar ones apart. One of the most popular CC algorithms is KwikCluster: an algorithm that serially clusters neighborhoods of vertices, and obtains a 3-approximation ratio. Unfortunately, KwikCluster in practice requires a large number of clustering rounds, a potential bottleneck for large graphs. We present C4 and ClusterWild!, two algorithms for parallel correlation clustering that run in a polylogarithmic number of rounds and achieve nearly linear speedups, provably. C4 uses concurrency control to enforce serializability of a parallel clustering process, and guarantees a 3-approximation ratio. ClusterWild! is a coordination free algorithm that abandons consistency for the benefit of better scaling; this leads to a provably small loss in the 3-approximation ratio. We provide extensive experimental results for both algorithms, where we outperform the state of the art, both in terms of clustering accuracy and running time. We show that our algorithms can cluster billion-edge graphs in under 5 seconds on 32 cores, while achieving a 15x speedup.