Fast Detection of Community Structures using Graph Traversal in Social Networks

TL;DR

This paper introduces a new graph traversal-based framework for community detection in social networks that is faster and produces higher quality clusters than existing methods like Louvain, suitable for real-time large-scale applications.

Contribution

A novel graph traversal approach that significantly improves speed and cluster quality in community detection compared to traditional algorithms.

Findings

Runs in linear time O(|V| + |E|) for initial clustering

Produces higher quality communities than Louvain on benchmark datasets

Suitable for real-time analysis of large social networks

Abstract

Finding community structures in social networks is considered to be a challenging task as many of the proposed algorithms are computationally expensive and does not scale well for large graphs. Most of the community detection algorithms proposed till date are unsuitable for applications that would require detection of communities in real-time, especially for massive networks. The Louvain method, which uses modularity maximization to detect clusters, is usually considered to be one of the fastest community detection algorithms even without any provable bound on its running time. We propose a novel graph traversal-based community detection framework, which not only runs faster than the Louvain method but also generates clusters of better quality for most of the benchmark datasets. We show that our algorithms run in O(|V | + |E|) time to create an initial cover before using modularity maximization to get the final cover. Keywords - community detection; Influenced Neighbor Score; brokers; community nodes; communities