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Dynamic-Sensitive centrality of nodes in temporal networks.

Da-Wen Huang1, Zu-Guo Yu1,2

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We introduce temporal Dynamic-Sensitive centrality (TDC) to identify influential nodes in dynamic networks. TDC accurately captures network dynamics, outperforming traditional centrality measures in real-world and theoretical network analyses.

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Area of Science:

  • Network Science
  • Complex Systems
  • Data Science

Background:

  • Identifying influential nodes is crucial for understanding network behavior.
  • Existing methods often overlook network dynamics, focusing on static topology.
  • Temporal networks, reflecting real-world interactions, require dynamic analysis.

Purpose of the Study:

  • To extend Dynamic-Sensitive centrality to temporal networks.
  • To develop a novel metric, temporal Dynamic-Sensitive centrality (TDC), for node influence.
  • To evaluate TDC's accuracy against existing centrality measures.

Main Methods:

  • Extending the Dynamic-Sensitive centrality concept to temporal networks.
  • Empirical analysis on three real-world temporal networks.
  • Theoretical analysis using a susceptible-infected-recovered (SIR) model on a temporal network.

Main Results:

  • Temporal Dynamic-Sensitive centrality (TDC) demonstrates superior accuracy compared to static and temporal versions of degree, closeness, and betweenness centrality.
  • TDC analysis reveals that both network topology and dynamics influence node spreading capabilities.
  • The impact of time-order on spreading influence is amplified when the spreading rate deviates from the epidemic threshold, particularly in temporal scale-free networks.

Conclusions:

  • TDC provides a more accurate measure of node influence in temporal networks by incorporating dynamics.
  • Network dynamics play a significant role in information or disease spreading.
  • Understanding the interplay between topology, dynamics, and time-order is key for predicting network behavior.