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Finding influential edges in multilayer networks: Perspective from multilayer diffusion model.

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Identifying influential edges in multiplex social networks is challenging due to heterogeneous connections. This study proposes a new method to pinpoint these critical edges, significantly reducing information diffusion when removed.

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Area of Science:

  • Network Science
  • Information Diffusion Models
  • Social Network Analysis

Background:

  • Multiplex social networks present challenges in identifying influential edges due to layer-specific heterogeneity.
  • Existing edge centrality measures struggle with the complex nature of multiplex networks.

Purpose of the Study:

  • To develop a general information diffusion model for multiplex networks using adjacency tensors.
  • To propose an efficient edge eigenvector centrality measure for identifying influential heterogeneous edges.
  • To demonstrate the effectiveness of the proposed method in controlling information diffusion.

Main Methods:

  • Developed a general information diffusion model based on adjacency tensors for multiplex networks.
  • Introduced an efficient edge eigenvector centrality to quantify edge influence in heterogeneous networks.
  • Utilized n-mode singular value to control information diffusion levels.

Main Results:

  • The proposed diffusion model effectively uses n-mode singular value to control information spread.
  • Efficient edge eigenvector centrality successfully identifies influential heterogeneous edges.
  • Numerical results on synthetic and real-world networks show superior performance over existing measures.

Conclusions:

  • Influential heterogeneous edges in multiplex networks can be identified by considering network layer centrality.
  • Removing top influential edges significantly curtails information diffusion across network layers.
  • The proposed method offers a robust approach for managing information flow in complex social networks.