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Locating Structural Centers: A Density-Based Clustering Method for Community Detection.

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This study introduces a novel density-based clustering method for uncovering network communities. The algorithm efficiently identifies community structures and node roles, outperforming existing state-of-the-art methods.

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Area of Science:

  • Network Science
  • Data Mining
  • Computational Social Science

Background:

  • Community structure detection is crucial for understanding complex networks.
  • Existing local expansion methods for community detection are sensitive to initial parameters.
  • Algorithmic identification of intrinsic network communities remains a significant challenge.

Purpose of the Study:

  • To present a novel local expansion method for community detection based on density-based clustering.
  • To uncover intrinsic network communities by identifying structural centers using a proposed structural centrality measure.
  • To address the sensitivity of existing methods to initial seeds and parameters.

Main Methods:

  • A density-based clustering approach is employed for community detection.
  • A novel structural centrality measure is proposed, considering local node density and relative distances.
  • Communities are expanded from identified structural centers using a single local search procedure and a heuristic strategy.
  • Node roles (cores and outliers) are identified by defining a border region.

Main Results:

  • The proposed method effectively uncovers intrinsic community structures.
  • The algorithm demonstrates efficiency in identifying complete community structures.
  • It successfully distinguishes between core nodes and outliers within communities.
  • Experimental results on real-world and artificial networks show comparative clustering performance and improved efficiency over state-of-the-art methods.

Conclusions:

  • The proposed density-based local expansion method offers an efficient and effective approach to community detection in complex networks.
  • The structural centrality measure and heuristic expansion strategy contribute to robust community uncovering and node role identification.
  • This method provides a valuable alternative to existing community detection algorithms, particularly in handling parameter sensitivity.