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Community Detection Using an Enhanced Louvain Method in Complex Networks

  • Laxmi ChaudharyEmail author
  • Buddha Singh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11319)

Abstract

Recent developments and extensive usage of social networking applications have facilitated enormous amounts of essential data. It can be examined for numerous reasons by companies, governments, nonprofit organizations. The problem in social networks analysis mainly emerged of its vast scale and complicated relations in the networks. These networks can be analyzed and visualized using community structure properties. This paper introduces an agglomerative hierarchical community detection approach, Enhanced Louvain method (ELM), to identify communities in complex networks. We proposed a modularity and similarity measure-based approach that does not need the information of the communities as input and can find community structure in a rapid way. Experimental results on real world network datasets demonstrate the performance of the proposed ELM method over its counterparts to show that good results can be generated. The performance of methods evaluated in terms of communities, modularity value and quality of community obtained in the network.

Keywords

Community detection Community structure Modularity Social network 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Jawaharlal Nehru UniversityNew DelhiIndia

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