Computing communities in large networks using random walks

TL;DR

This paper introduces a new random walk-based similarity measure for detecting communities in large networks, offering improved efficiency and quality over previous methods.

Contribution

It presents a novel community detection algorithm using random walk similarities that is both computationally efficient and effective in identifying community structures.

Findings

Algorithm outperforms previous methods in community quality

Runs efficiently on large networks with real-world data

Achieves better results in less time

Abstract

Dense subgraphs of sparse graphs (communities), which appear in most real-world complex networks, play an important role in many contexts. Computing them however is generally expensive. We propose here a measure of similarities between vertices based on random walks which has several important advantages: it captures well the community structure in a network, it can be computed efficiently, it works at various scales, and it can be used in an agglomerative algorithm to compute efficiently the community structure of a network. We propose such an algorithm which runs in time O(mn^2) and space O(n^2) in the worst case, and in time O(n^2log n) and space O(n^2) in most real-world cases (n and m are respectively the number of vertices and edges in the input graph). Experimental evaluation shows that our algorithm surpasses previously proposed ones concerning the quality of the obtained community structures and that it stands among the best ones concerning the running time. This is very promising because our algorithm can be improved in several ways, which we sketch at the end of the paper.