The ParClusterers Benchmark Suite (PCBS): A Fine-Grained Analysis of Scalable Graph Clustering

TL;DR

The paper introduces the ParClusterers Benchmark Suite (PCBS), a comprehensive toolkit for evaluating scalable graph clustering algorithms across various use cases, improving comparison fairness and understanding of quality-performance tradeoffs.

Contribution

It provides a standardized benchmarking suite for scalable graph clustering algorithms, including diverse algorithms and evaluation tools for quality and performance assessment.

Findings

Best quality results come from algorithms not in common toolkits.

PCBS enables fair comparison of clustering algorithms.

It facilitates fine-tuning and evaluation of clustering methods.

Abstract

We introduce the ParClusterers Benchmark Suite (PCBS) -- a collection of highly scalable parallel graph clustering algorithms and benchmarking tools that streamline comparing different graph clustering algorithms and implementations. The benchmark includes clustering algorithms that target a wide range of modern clustering use cases, including community detection, classification, and dense subgraph mining. The benchmark toolkit makes it easy to run and evaluate multiple instances of different clustering algorithms, which can be useful for fine-tuning the performance of clustering on a given task, and for comparing different clustering algorithms based on different metrics of interest, including clustering quality and running time. Using PCBS, we evaluate a broad collection of real-world graph clustering datasets. Somewhat surprisingly, we find that the best quality results are obtained by algorithms that not included in many popular graph clustering toolkits. The PCBS provides a standardized way to evaluate and judge the quality-performance tradeoffs of the active research area of scalable graph clustering algorithms. We believe it will help enable fair, accurate, and nuanced evaluation of graph clustering algorithms in the future.