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Updated: Jun 20, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Temporal local clustering coefficient uncovers the hidden pattern in temporal networks.

Bofan Chen1,2, Guyu Hou1,3, Aming Li1,4

  • 1Center for Systems and Control, College of Engineering, <a href="https://ror.org/02v51f717">Peking University</a>, Beijing 100871, People's Republic of China.

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Summary

This study introduces the temporal local clustering coefficient (TC) for analyzing dynamic complex networks. TC reveals distinct interaction patterns in temporal networks, differing from static network analysis.

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Area of Science:

  • Complex network theory
  • Network science
  • Data analysis

Background:

  • Understanding complex networks relies on topological characteristics.
  • Temporal networks require analysis beyond static models, incorporating time-varying interactions.
  • Classical metrics like clustering coefficient (C) are insufficient for temporal dynamics.

Purpose of the Study:

  • To extend the local clustering coefficient analysis to temporal networks.
  • To introduce and analyze the temporal local clustering coefficient (TC).
  • To uncover hidden information in nodes' connectance during time-varying interactions.

Main Methods:

  • Extension of the traditional local clustering coefficient (C) for static networks.
  • Development and application of the temporal local clustering coefficient (TC) for temporal networks.
  • Systematic analysis of various empirical datasets.

Main Results:

  • The temporal local clustering coefficient (TC) captures information on node connectance rhythms.
  • TC reveals different interaction patterns across various temporal network types.
  • TC correlates strongly with C in efficiency networks but not in social activity networks.
  • TC differentiates actual clustering properties from accidental interactions.

Conclusions:

  • The temporal local clustering coefficient (TC) is crucial for understanding temporal network structures.
  • Dynamical characteristics are fundamental for a complete understanding of real complex systems.
  • TC provides insights into network behavior distinct from static analyses.