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An optimization algorithm for maximum quasi-clique problem based on information feedback model.

Shuhong Liu1, Jincheng Zhou2,3, Dan Wang3

  • 1State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, Guizhou, China.

Peerj. Computer Science
|August 15, 2024
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Summary
This summary is machine-generated.

This study introduces NF1 and NR1 algorithms for the maximum quasi-clique problem, improving performance on dense graphs by using an information feedback model. These methods enhance analysis in bioinformatics and social networks.

Keywords:
Historical iterationInformation feedback modelMetaheuristic algorithmγ-quasi-clique

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Area of Science:

  • Graph Theory
  • Computational Complexity
  • Network Analysis

Background:

  • The maximum clique problem, identifying the largest complete subgraph, is NP-hard.
  • The quasi-clique model offers a practical relaxation with applications in bioinformatics and social network analysis.

Purpose of the Study:

  • To introduce and evaluate two novel algorithms, NF1 and NR1, for solving the maximum quasi-clique problem.
  • To enhance the efficiency and applicability of quasi-clique detection in complex networks.

Main Methods:

  • Development of NF1 and NR1 algorithms incorporating an information feedback model.
  • Utilizing fitness weighting to calculate information feedback scores.
  • Updating individuals based on a benchmark algorithm and historical data from previous iterations.

Main Results:

  • Both NF1 and NR1 algorithms demonstrated superior performance compared to the benchmark algorithm on dense graph instances.
  • Comparable performance was achieved by NF1 and NR1 on sparse graph instances.
  • Experimental validation was conducted on numerous composite and real-world graphs.

Conclusions:

  • The proposed NF1 and NR1 algorithms offer an effective approach to the maximum quasi-clique problem.
  • The information feedback model enhances algorithm performance, particularly in dense network structures.
  • These algorithms provide valuable tools for analyzing complex networks in fields like bioinformatics and social network analysis.