期刊
KNOWLEDGE AND INFORMATION SYSTEMS
卷 59, 期 1, 页码 1-31出版社
SPRINGER LONDON LTD
DOI: 10.1007/s10115-018-1209-7
关键词
Community detection; Influenced Neighbor Score; Brokers; Community nodes; Communities
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据