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Influence maximization in multilayer networks based on adaptive coupling degree.

Su-Su Zhang1, Ming Xie1, Chuang Liu1

  • 1Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, China.

Chaos (Woodbury, N.Y.)
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Summary
This summary is machine-generated.

This study introduces a new method for influence maximization (IM) in multilayer networks. The adaptive coupling degree (ACD) algorithm effectively identifies key nodes to spread information across interconnected layers.

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Area of Science:

  • Network Science
  • Computer Science
  • Data Science

Background:

  • Influence maximization (IM) traditionally focuses on single-layer networks.
  • Multilayer networks possess complex coupling structures often overlooked in prior IM research.
  • Understanding influence spread in interconnected layers is crucial for modern network analysis.

Purpose of the Study:

  • To address the challenge of influence maximization in multilayer networks.
  • To propose a novel model and heuristic algorithm for IM in multilayer settings.
  • To evaluate the effectiveness of the proposed approach against existing methods.

Main Methods:

  • Introduced a multilayer independent cascade (MIC) model for simultaneous propagation across network layers.
  • Developed the adaptive coupling degree (ACD) heuristic algorithm for seed node selection.
  • Conducted experiments on synthetic and real-world multilayer networks to validate the approach.

Main Results:

  • The proposed MIC model and ACD algorithm demonstrated superior performance in influence spread compared to baseline methods.
  • The ACD algorithm showed efficiency in terms of time cost.
  • Experimental results were consistent across diverse network structures.

Conclusions:

  • The proposed MIC model and ACD algorithm offer an effective solution for influence maximization in multilayer networks.
  • The study highlights the importance of considering network coupling structures for accurate IM.
  • The findings provide a valuable contribution to network analysis and information diffusion research.