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Related Experiment Video

Updated: Jan 12, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Hyper-aware adaptive heuristic algorithms: A novel approach for seed selection in hypergraph-based diffusion.

Dandan Zhao1, Yongqi Zhang1, Bo Zhang2

  • 1School of Computer Science and Technology, Zhejiang Normal University, Jinhua, Zhejiang 321004, China.

Chaos (Woodbury, N.Y.)
|November 3, 2025
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Summary
This summary is machine-generated.

This study introduces hypergraph-based influence maximization (IM) algorithms for complex networks. A novel hyperdegree-scaled method optimizes diffusion performance and efficiency, outperforming baselines.

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Area of Science:

  • Network Science
  • Computational Social Science
  • Epidemiology

Background:

  • Influence maximization (IM) is crucial for network analysis but traditional graph models struggle with higher-order interactions.
  • Hypergraphs offer a more expressive framework for multi-node relationships, essential for complex systems.

Purpose of the Study:

  • To investigate influence maximization on hypergraphs using the Susceptible-Infected (SI) model with Contact Process dynamics.
  • To develop and evaluate novel hyper-aware adaptive heuristic algorithms for improved diffusion.

Main Methods:

  • Proposed four hyper-aware adaptive heuristic algorithms leveraging node degree and hyperdegree.
  • Analyzed the impact of seed node influence on first- and second-order neighbors.
  • Conducted experiments on real-world and synthetic hypergraphs with varying heterogeneity.

Main Results:

  • The proposed algorithms consistently outperformed baseline methods in diffusion effectiveness, especially under limited seed budgets.
  • The hyperdegree-scaled 1st-order neighbor reduction algorithm demonstrated an optimal balance between performance and efficiency.
  • Achieved up to 30.29% improvement in maximum effectiveness and 63.42% under constrained budgets.

Conclusions:

  • Hypergraph-based IM approaches are superior to traditional graph methods for complex interactions.
  • The developed hyper-aware algorithms offer significant improvements in diffusion effectiveness and computational efficiency.
  • The hyperdegree-scaled 1st-order neighbor reduction algorithm is a promising strategy for practical influence maximization.