Multi-resolution community detection in massive networks

TL;DR

This paper introduces a fast, multi-resolution community detection algorithm that identifies hierarchical structures in large networks without prior information or optimization, demonstrating strong results on synthetic, real-world, and biological networks.

Contribution

The proposed method detects communities efficiently at multiple scales without prior knowledge or objective function optimization, revealing hierarchical network structures.

Findings

Performs well and runs fast on synthetic and real networks

Reveals hierarchical community structures through resolution tuning

Identifies functionally coherent modules in biological networks

Abstract

Aiming at improving the efficiency and accuracy of community detection in complex networks, we proposed a new algorithm, which is based on the idea that communities could be detected from subnetworks by comparing the internal and external cohesion of each subnetwork. In our method, similar nodes are firstly gathered into meta-communities, which are then decided to be retained or merged through a multilevel label propagation process, until all of them meet our community criterion. Our algorithm requires neither any priori information of communities nor optimization of any objective function. Experimental results on both synthetic and real-world networks show that, our algorithm performs quite well and runs extremely fast, compared with several other popular algorithms. By tuning a resolution parameter, we can also observe communities at different scales, so this could reveal the hierarchical structure of the network. To further explore the effectiveness of our method, we applied it to the E-Coli transcriptional regulatory network, and found that all the identified modules have strong structural and functional coherence.