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The 7th International Conference on Contemporary Issues in Data Science

CiDaS 2019: Data Science: From Research to Application pp 105–120Cite as

A Community Detection Method Based on the Subspace Similarity of Nodes in Complex Networks

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Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 45))

Abstract

Many real-world networks have a topological structure characterized by cohesive groups of vertices. Community detection aims at identifying such groups and plays a critical role in network science. Till now, many community detection methods have been developed in the literature. Most of them require to know the number of communities and the low accuracy in the complex networks are the shortcomings of most of these methods. To tackle these issues in this paper, a novel community detection method called CDNSS is proposed. The proposed method is based on the nodes subspace similarity and includes two main phases; seeding and expansion. In the first phase, seeds are identified using the potential distribution in the local and global similarity space. To compute the similarity between each pair, a specific centrality measure by considering the sparse linear coding and self-expressiveness ability of nodes. Then, the nodes with best focal state are discovered which guarantees the stability of solutions. In the expansion phase, a greedy strategy is used to assign the unlabeled nods to the relevant focal regions. The results of the experiments performed on several real-world and synthetic networks confirm the superiority of the proposed method in comparison with well-known and state-of-the-art community detection methods.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran

    Mehrnoush Mohammadi & Parham Moradi

  2. School of Engineering, RMIT University, Melbourne, Australia

    Mahdi Jalili

Authors
  1. Mehrnoush Mohammadi
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  2. Parham Moradi
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  3. Mahdi Jalili
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Corresponding author

Correspondence to Parham Moradi .

Editor information

Editors and Affiliations

  1. Institute for Advanced Studies in Basic Science, Zanjan, Iran

    Mahdi Bohlouli

  2. Institute for Advanced Studies in Basic Science, Zanjan, Iran

    Bahram Sadeghi Bigham

  3. Institute for Advanced Studies in Basic Science, Zanjan, Iran

    Zahra Narimani

  4. Institute for Advanced Studies in Basic Science, Zanjan, Iran

    Mahdi Vasighi

  5. Institute for Advanced Studies in Basic Science, Zanjan, Iran

    Ebrahim Ansari

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Mohammadi, M., Moradi, P., Jalili, M. (2020). A Community Detection Method Based on the Subspace Similarity of Nodes in Complex Networks. In: Bohlouli, M., Sadeghi Bigham, B., Narimani, Z., Vasighi, M., Ansari, E. (eds) Data Science: From Research to Application. CiDaS 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-030-37309-2_9

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