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Inferring Centrality from Network Snapshots.

Haibin Shao1, Mehran Mesbahi2, Dewei Li1

  • 1Department of Automation, Shanghai Jiao Tong University and the Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.

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Summary
This summary is machine-generated.

We introduce tempo centrality to measure node influence in consensus networks using only dynamic data. This method accurately estimates influence propagation, graph properties, and disturbance rejection, even with incomplete network topology.

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Area of Science:

  • Network science
  • Complex systems analysis
  • Graph theory

Background:

  • Network topology significantly influences functionality, but is often unknown for large-scale systems.
  • Inferring network properties from dynamics is crucial when explicit topology is unavailable.

Purpose of the Study:

  • To propose and validate 'tempo centrality' as a metric for node influence in consensus networks.
  • To demonstrate that tempo centrality can be inferred from network dynamics data alone.
  • To link tempo centrality to key network performance indicators.

Main Methods:

  • Utilizing network dynamics data to infer tempo centrality.
  • Developing tempo centrality as a measure of node influence.
  • Correlating tempo centrality with influence propagation rates and Kirchhoff index.
  • Analyzing tempo centrality's relation to disturbance rejection.

Main Results:

  • Tempo centrality accurately quantifies node influence in consensus networks.
  • Inferred tempo centrality provides precise estimates of influence propagation rates.
  • Tempo centrality effectively predicts the Kirchhoff index of the underlying graph.
  • Tempo centrality reveals insights into the disturbance rejection capabilities of network nodes.

Conclusions:

  • Tempo centrality offers a powerful method for analyzing consensus networks using only dynamic data.
  • This approach bypasses the need for complete network topology information.
  • The findings enable inference of network performance from temporal data, advancing network analysis.