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Directed closure coefficient and its patterns.

Mingshan Jia1, Bogdan Gabrys1, Katarzyna Musial1

  • 1School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia.

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Summary
This summary is machine-generated.

We introduce new methods to measure directed triangle formation in complex networks. These directed closure coefficients improve network classification and link prediction accuracy.

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Area of Science:

  • Network Science
  • Graph Theory
  • Data Mining

Background:

  • Triangle structures are fundamental in complex network analysis.
  • The clustering coefficient measures triangle formation from a center-node perspective.
  • The closure coefficient offers an end-node perspective on triangle formation.

Purpose of the Study:

  • To extend the closure coefficient for directed networks.
  • To introduce directed closure, source closure, and target closure coefficients.
  • To define four closure patterns for directed triangles.

Main Methods:

  • Proposing directed closure coefficient to quantify directed triangle formation.
  • Introducing source and target closure coefficients based on edge direction.
  • Categorizing specific directed triangle types into four closure patterns.
  • Conducting experiments on 24 directed networks across six domains.

Main Results:

  • The four closure patterns serve as distinctive network-level features for network type classification.
  • Incorporating source and target closure coefficients significantly enhances link prediction performance at the node level for most directed networks.

Conclusions:

  • Directed closure coefficients offer valuable insights into network structure and dynamics.
  • These coefficients improve both network classification and predictive accuracy in link prediction tasks.