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The transsortative structure of networks.

Shin-Chieng Ngo1,2, Allon G Percus2,3, Keith Burghardt2

  • 1Department of Physics and Astronomy, University of Southern California, Los Angeles, CA 90089, USA.

Proceedings. Mathematical, Physical, and Engineering Sciences
|June 12, 2020
PubMed
Summary
This summary is machine-generated.

Researchers introduced transsortativity, a new network property measuring neighbor correlations. This metric, independent of degree distribution and assortativity, impacts contagion spread and social perceptions, enabling more realistic network models.

Keywords:
multi-hop structurenetwork sciencerandom networks

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Area of Science:

  • Complex systems analysis
  • Network science
  • Computational social science

Background:

  • Network topologies are complex and not fully captured by local node statistics like degree assortativity.
  • Existing network models lack metrics for neighbor correlations, limiting realism.

Purpose of the Study:

  • To introduce and define a novel network property, transsortativity, to quantify correlations among a node's neighbors.
  • To demonstrate that transsortativity is independent of degree distribution and assortativity.
  • To highlight the impact of transsortativity on network processes and perceptions.

Main Methods:

  • Definition of transsortativity as a measure of neighbor correlations.
  • Analysis of transsortativity's independence from other network properties (degree distribution, assortativity).
  • Investigation of transsortativity's effects on contagion spread and majority illusion phenomena.

Main Results:

  • Transsortativity is a quantifiable network property distinct from degree assortativity.
  • This property can be varied independently of degree distribution and assortativity.
  • Transsortativity significantly influences contagion dynamics and the majority illusion.

Conclusions:

  • Transsortativity offers a new dimension for characterizing complex networks.
  • Incorporating transsortativity enhances the realism of network models.
  • This metric improves our ability to analyze and simulate network behaviors.