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DCC: A Cascade-Based Approach to Detect Communities in Social Networks

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High Performance Computing, Smart Devices and Networks (CHSN 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1087))

Abstract

Community detection in social networks is associated with finding and grouping the most similar nodes inherent in the network. These similar nodes are identified by computing tie strength. Stronger ties indicate higher proximity shared by connected node pairs. This work is motivated by Granovetter’s argument that suggests that strong ties lie within densely connected nodes and the theory that community cores in real-world networks are densely connected. In this paper, we have introduced a novel method called Disjoint Community detection using Cascades (DCC) which demonstrates the effectiveness of a new local density-based tie strength measure on detecting communities. Here, tie strength is utilized to decide the paths followed for propagating information. The idea is to crawl through the tuple information of cascades toward the community core guided by increasing tie strength. Considering the cascade generation step, a novel Preferential Membership method has been developed to assign community labels to unassigned nodes. The efficacy of DCC has been analyzed based on quality and accuracy on several real-world datasets and baseline community detection algorithms.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar, Assam, India

    Soumita Das & Anupam Biswas

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    Akrati Saxena

Authors
  1. Soumita Das
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  2. Anupam Biswas
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  3. Akrati Saxena
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Corresponding author

Correspondence to Soumita Das .

Editor information

Editors and Affiliations

  1. Delhi Technological University, Delhi, India

    Ruchika Malhotra

  2. Jawaharlal Nehru Technological University Kakinada, Kakinada, Andhra Pradesh, India

    L. Sumalatha

  3. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia

    S. M. Warusia Yassin

  4. National Institute of Technology Silchar, Silchar, Assam, India

    Ripon Patgiri

  5. National Institute of Technology Silchar, Silchar, Assam, India

    Naresh Babu Muppalaneni

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Das, S., Biswas, A., Saxena, A. (2024). DCC: A Cascade-Based Approach to Detect Communities in Social Networks. In: Malhotra, R., Sumalatha, L., Yassin, S.M.W., Patgiri, R., Muppalaneni, N.B. (eds) High Performance Computing, Smart Devices and Networks. CHSN 2022. Lecture Notes in Electrical Engineering, vol 1087. Springer, Singapore. https://doi.org/10.1007/978-981-99-6690-5_28

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  • DOI: https://doi.org/10.1007/978-981-99-6690-5_28

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-6689-9

  • Online ISBN: 978-981-99-6690-5

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