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GTIP: A Gaming-Based Topic Influence Percolation Model for Semantic Overlapping Community Detection.

Hailu Yang1, Jin Zhang2, Xiaoyu Ding3

  • 1School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150001, China.

Entropy (Basel, Switzerland)
|September 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Gaming-based Topic Influence Percolation model (GTIP) for semantic overlapping community detection. GTIP enhances community discovery by considering topic influence and propagation, outperforming existing methods in accuracy and semantic modularity.

Keywords:
community detectiongame theorypercolation mechanicssemantic social networkstopic influence

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Area of Science:

  • Social Network Analysis
  • Computational Social Science
  • Data Mining

Background:

  • Semantic social network analysis is vital for understanding online interactions.
  • Existing methods often overlook topic influence and propagation dynamics in community detection.
  • Accurate community detection is essential for various applications.

Purpose of the Study:

  • To propose a new model for semantic overlapping community detection.
  • To incorporate topic influence and propagation into community formation.
  • To address limitations of conventional semantic community detection methods.

Main Methods:

  • Developed a Gaming-based Topic Influence Percolation model (GTIP).
  • Modeled community formation as a seed expansion process using a modified percolation approach.
  • Integrated game theory concepts (payoff) to regulate topic acceptance among neighbors.

Main Results:

  • GTIP demonstrated superior performance compared to traditional and representative methods.
  • The model achieved higher accuracy in identifying ground truth communities in synthetic networks.
  • GTIP exhibited enhanced semantic modularity in real-world social networks.

Conclusions:

  • The GTIP model offers a more realistic approach to semantic overlapping community detection.
  • Considering topic influence and propagation significantly improves community discovery.
  • GTIP provides a promising advancement for analyzing complex social network structures.