A Starting Point for Dynamic Community Detection with Leiden Algorithm

TL;DR

This paper introduces and evaluates three dynamic community detection methods integrated with the Leiden algorithm, demonstrating significant speedups on evolving large graphs using multicore processing.

Contribution

It presents the first application of dynamic approaches to the Leiden algorithm, enhancing its efficiency for evolving graph data.

Findings

ND, DS, and DF Leiden achieve up to 1.98x speedup.

Speedup scales with the number of threads, reaching 1.6x per doubling.

Experiments conducted on a 64-core server with large graphs.

Abstract

Real-world graphs often evolve over time, making community or cluster detection a crucial task. In this technical report, we extend three dynamic approaches - Naive-dynamic (ND), Delta-screening (DS), and Dynamic Frontier (DF) - to our multicore implementation of the Leiden algorithm, known for its high-quality community detection. Our experiments, conducted on a server with a 64-core AMD EPYC-7742 processor, show that ND, DS, and DF Leiden achieve average speedups of 1.37x, 1.47x, and 1.98x on large graphs with random batch updates, compared to the Static Leiden algorithm - while scaling at a rate of 1.6x for every doubling of threads. To our knowledge, this is the first attempt to apply dynamic approaches to the Leiden algorithm. We hope these early results pave the way for further development of dynamic approaches for evolving graphs.