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COMICS: a community property-based triangle motif clustering scheme.

Yufan Feng1, Shuo Yu1, Kaiyuan Zhang1

  • 1School of Software, Dalian University of Technology, Dalian, China.

Peerj. Computer Science
|April 5, 2021
PubMed
Summary

This study introduces COMICS, a novel network analysis method using triangle motifs for efficient community detection. COMICS improves accuracy and speed in partitioning large-scale networks like co-authorship and social networks.

Keywords:
ClusteringCommunity propertyLarge networkTriangle motif

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Area of Science:

  • Network Science
  • Data Mining
  • Computational Social Science

Background:

  • Rapid growth in network data across various fields (biology, economics, social sciences).
  • Network analysis often involves complex partition and community detection problems.
  • Existing methods may struggle with scalability and accuracy on large networks.

Purpose of the Study:

  • To develop a community property-based triangle motif clustering scheme (COMICS).
  • To enhance the efficiency and accuracy of network partition and community detection.
  • To explore the utility of triangle motifs in bridging network structures and statistical data.

Main Methods:

  • COMICS utilizes graph partitioning and triangle motif-based clustering.
  • Four network cutting conditions are applied to divide large networks into dense subgraphs.
  • Triangle motifs are used to refine and specify the partition results.

Main Results:

  • COMICS demonstrated superior performance in both runtime and accuracy compared to other methods.
  • Experiments were conducted on large-scale co-authorship (APS, MAG) and social networks (Facebook, gemsec-Deezer).
  • Triangle motifs effectively connected network structures with statistical data in academic collaboration networks.

Conclusions:

  • COMICS offers an efficient and accurate approach for community detection in large-scale networks.
  • Triangle motif analysis provides valuable insights into network structures and their relationship with statistical data.
  • The method shows significant potential for applications in various network analysis domains.