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Influence maximization based on threshold models in hypergraphs.

Renquan Zhang1, Xilong Qu1, Qiang Zhang2

  • 1School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China.

Chaos (Woodbury, N.Y.)
|February 16, 2024
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Summary
This summary is machine-generated.

This study introduces a framework for collective influence in hypergraphs to identify key nodes for maximizing spread in threshold models. The proposed HCI-TM algorithm effectively selects influential nodes, outperforming existing methods.

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Area of Science:

  • Complex Networks
  • Network Science
  • Computational Social Science

Background:

  • Influence maximization is crucial for applications like viral marketing and disease control.
  • Existing models often focus on pairwise networks, limiting applicability to complex systems.

Purpose of the Study:

  • To develop a theoretical framework for collective influence in hypergraphs using threshold models.
  • To introduce a novel algorithm for influence maximization in hypergraphs.

Main Methods:

  • Extending message passing methods to hypergraphs for threshold models.
  • Introducing the Hypergraph Collective Influence (HCI) metric.
  • Developing the HCI-TM algorithm for seed set selection.

Main Results:

  • The HCI-TM algorithm demonstrates superior performance compared to existing methods on synthetic and real-world hypergraphs.
  • Hypergraph Collective Influence (HCI) can predict cascading phenomena.
  • Algorithm performance is sensitive to hypergraph properties like average hyperdegree and scale-free exponents.

Conclusions:

  • The proposed framework and HCI-TM algorithm offer an effective approach for influence maximization in hypergraphs.
  • HCI provides a valuable tool for understanding and predicting large-scale information or disease spread.
  • Understanding hypergraph structure is key to optimizing influence maximization strategies.