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Evaluating link prediction by diffusion processes in dynamic networks.

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Link prediction (LP) improves network spreading by adding new connections. Evolved scale-free networks with LP show enhanced information diffusion, lower shortest paths, and fewer structural holes.

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Area of Science:

  • Network Science
  • Complex Systems
  • Computational Social Science

Background:

  • Link prediction (LP) infers missing or future network connections.
  • Network structure influences information spreading and evolution.
  • Existing LP evaluations lack systematic analysis of spreading dynamics and structural evolution.

Purpose of the Study:

  • To systematically analyze link prediction algorithms' effects on network spreading and structural evolution.
  • To identify LP methods that best enhance network spreading through added links.
  • To investigate the structural properties of networks evolved via link prediction.

Main Methods:

  • Framework incorporating diffusion processes (Epidemics, Information, Rumor models).
  • Evaluation of various link prediction methods for their impact on spreading.
  • Analysis of structural properties of LP-evolved networks using extensive numerical simulations on diverse datasets.

Main Results:

  • Link prediction enhances spreading in evolved scale-free networks.
  • Improved spreading correlates with lower shortest-path lengths and fewer structural holes.
  • Network properties like triangles, modularity, assortativity, and coreness do not consistently increase propagation.

Conclusions:

  • Link prediction significantly impacts network structure and spreading dynamics.
  • The study provides a practical guide for selecting and evaluating LP methods based on computational cost, spreading capacity, and network structure.
  • Understanding LP's role in network evolution is crucial for optimizing information diffusion.