Knowledge and Information Systems

, Volume 59, Issue 1, pp 1–31 | Cite as

Fast detection of community structures using graph traversal in social networks

  • Partha BasuchowdhuriEmail author
  • Satyaki Sikdar
  • Varsha Nagarajan
  • Khusbu Mishra
  • Surabhi Gupta
  • Subhashis Majumder
Regular Paper


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.


Community detection Influenced Neighbor Score Brokers Community nodes Communities 



We would like to thank the reviewers for their effort for thoroughly reading our paper and for suggesting valuable changes. We would also like to thank Upasana Dutta for pointing out and correcting errors in some of the figures and algorithms that appear in this work.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringHeritage Institute of TechnologyKolkataIndia
  2. 2.Department of Computer Science and EngineeringUniversity of Notre DameNotre DameUSA

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