LazyFox: Fast and parallelized overlapping community detection in large graphs

TL;DR

LazyFox is a multi-threaded algorithm that significantly speeds up overlapping community detection in large graphs without sacrificing quality, enabling analysis of datasets with millions of nodes and billions of edges in days.

Contribution

It introduces LazyFox, a parallelized version of the Fox algorithm, achieving faster overlapping community detection on large graphs while maintaining accuracy.

Findings

LazyFox detects communities in graphs with millions of nodes within days.

It maintains community quality comparable to the original Fox algorithm.

LazyFox scales efficiently with graph size and complexity.

Abstract

The detection of communities in graph datasets provides insight about a graph's underlying structure and is an important tool for various domains such as social sciences, marketing, traffic forecast, and drug discovery. While most existing algorithms provide fast approaches for community detection, their results usually contain strictly separated communities. However, most datasets would semantically allow for or even require overlapping communities that can only be determined at much higher computational cost. We build on an efficient algorithm, Fox, that detects such overlapping communities. Fox measures the closeness of a node to a community by approximating the count of triangles which that node forms with that community. We propose LazyFox, a multi-threaded version of the Fox algorithm, which provides even faster detection without an impact on community quality. This allows for the analyses of significantly larger and more complex datasets. LazyFox enables overlapping community detection on complex graph datasets with millions of nodes and billions of edges in days instead of weeks. As part of this work, LazyFox's implementation was published and is available as a tool under an MIT licence at https://github.com/TimGarrels/LazyFox.