Fast Detection of Community Structures using Graph Traversal in Social Networks
This addresses the need for real-time community detection in massive networks, offering a faster and more effective solution than existing methods.
The paper tackles the problem of fast community detection in large social networks by proposing a graph traversal-based framework that runs faster than the Louvain method and produces higher-quality clusters on benchmark datasets, with algorithms operating in O(|V| + |E|) time for initial cover generation.
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