MRAttractor: Detecting Communities from Large-Scale Graphs

TL;DR

MRAttractor is an improved community detection algorithm based on Attractor, optimized for large-scale graphs using MapReduce, achieving faster runtimes without sacrificing detection quality.

Contribution

This paper introduces MRAttractor, a scalable MapReduce implementation of Attractor with a sliding window technique to reduce runtime on large graphs.

Findings

Significantly reduced running time compared to original Attractor.

Able to handle large-scale graphs effectively.

Maintained community detection quality with the new method.

Abstract

Detecting groups of users, who have similar opinions, interests, or social behavior, has become an important task for many applications. A recent study showed that dynamic distance based Attractor, a community detection algorithm, outperformed other community detection algorithms such as Spectral clustering, Louvain and Infomap, achieving higher Normalized Mutual Information (NMI) and Adjusted Rand Index (ARI). However, Attractor often takes long time to detect communities, requiring many iterations. To overcome the drawback and handle large-scale graphs, in this paper we propose MRAttractor, an advanced version of Attractor to be runnable on a MapReduce framework. In particular, we (i) apply a sliding window technique to reduce the running time, keeping the same community detection quality; (ii) design and implement the Attractor algorithm for a MapReduce framework; and (iii) evaluate MRAttractor's performance on synthetic and real-world datasets. Experimental results show that our algorithm significantly reduced running time and was able to handle large-scale graphs.