Parallel Local Graph Clustering

TL;DR

This paper introduces parallel algorithms for local graph clustering, significantly improving efficiency on large graphs by leveraging multicore processing, with theoretical analysis and empirical validation showing substantial speedups.

Contribution

It develops and analyzes parallel versions of local graph clustering algorithms for shared-memory multicore systems, enhancing their practical efficiency.

Findings

Achieves good parallel speedups on large-scale graphs

Parallel algorithms significantly reduce clustering computation time

Experimental results confirm efficiency gains on multicore machines

Abstract

Graph clustering has many important applications in computing, but due to growing sizes of graphs, even traditionally fast clustering methods such as spectral partitioning can be computationally expensive for real-world graphs of interest. Motivated partly by this, so-called local algorithms for graph clustering have received significant interest due to the fact that they can find good clusters in a graph with work proportional to the size of the cluster rather than that of the entire graph. This feature has proven to be crucial in making such graph clustering and many of its downstream applications efficient in practice. While local clustering algorithms are already faster than traditional algorithms that touch the entire graph, they are sequential and there is an opportunity to make them even more efficient via parallelization. In this paper, we show how to parallelize many of these algorithms in the shared-memory multicore setting, and we analyze the parallel complexity of these algorithms. We present comprehensive experiments on large-scale graphs showing that our parallel algorithms achieve good parallel speedups on a modern multicore machine, thus significantly speeding up the analysis of local graph clusters in the very large-scale setting.