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Density-based and transport-based core-periphery structures in networks.

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Summary
This summary is machine-generated.

This study explores core-periphery structures in various networks, finding a transport-based node coreness measure effective for transportation systems. This analysis enhances understanding of network structure and function across diverse domains.

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Area of Science:

  • Network Science
  • Graph Theory
  • Data Analysis

Background:

  • Mesoscale structures in networks offer insights into system organization and function.
  • While community structure is widely studied, other mesoscale patterns like core-periphery structures are also significant.
  • Core-periphery structures feature highly connected core nodes and isolated peripheral nodes.

Purpose of the Study:

  • To investigate core-periphery structures in diverse network types (transportation, social, financial).
  • To evaluate the utility of a transport-based node coreness measure for characterizing networks.
  • To generalize coreness concepts to network edges and develop generative models for road-like networks.

Main Methods:

  • Analysis of core-periphery structures using density and transport metrics.
  • Application of a transport-based node coreness measure to real-world networks.
  • Generalization of coreness to edges and development of generative network models.
  • Examination of embedding space dimensionality effects on network properties.

Main Results:

  • Core-periphery structures were identified across transportation, social, and financial networks.
  • A transport-based node coreness measure proved effective for characterizing transportation networks.
  • The generalized edge coreness diagnostic also showed utility for transportation networks.
  • Generative models revealed the impact of embedding space dimensionality on network characteristics.

Conclusions:

  • Core-periphery structures are prevalent and informative across various network types.
  • Transport-based coreness is a valuable tool for analyzing transportation network topology.
  • Network measures can exhibit distinct behaviors depending on network type and underlying generative processes.