DF Louvain: Fast Incrementally Expanding Approach for Community Detection on Dynamic Graphs

TL;DR

The paper introduces DF Louvain, a fast parallel algorithm for community detection in dynamic graphs that efficiently updates communities with minimal overhead during graph changes.

Contribution

It presents a novel incremental approach for community detection on dynamic graphs, including parallel implementations and significant speedup results.

Findings

DF Louvain achieves up to 179x speedup over static methods.

The algorithm scales well with increased threads, gaining 1.6x performance per doubling.

Experimental results demonstrate substantial efficiency improvements on real-world and large graphs.

Abstract

Community detection is the problem of recognizing natural divisions in networks. A relevant challenge in this problem is to find communities on rapidly evolving graphs. In this report we present our Parallel Dynamic Frontier (DF) Louvain algorithm, which given a batch update of edge deletions and insertions, incrementally identifies and processes an approximate set of affected vertices in the graph with minimal overhead, while using a novel approach of incrementally updating weighted-degrees of vertices and total edge weights of communities. We also present our parallel implementations of Naive-dynamic (ND) and Delta-screening (DS) Louvain. On a server with a 64-core AMD EPYC-7742 processor, our experiments show that DF Louvain obtains speedups of 179x, 7.2x, and 5.3x on real-world dynamic graphs, compared to Static, ND, and DS Louvain, respectively, and is 183x, 13.8x, and 8.7x faster, respectively, on large graphs with random batch updates. Moreover, DF Louvain improves its performance by 1.6x for every doubling of threads.