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Lifetime-preserving reference models for characterizing spreading dynamics on temporal networks.

Mingwu Li1, Vikyath D Rao2,3, Tim Gernat2,4

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Summary
This summary is machine-generated.

Randomized reference models for temporal networks are crucial but often fail to preserve node and edge lifespans. New models (PTN, PTE) that preserve these lifetimes are essential for accurate spreading dynamics analysis in real-world networks with dynamic node/edge turnover.

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Area of Science:

  • Network Science
  • Complex Systems
  • Computational Social Science

Background:

  • Temporal networks are crucial for understanding dynamic processes.
  • Randomized reference models like permuted times (PT) are used to study network features.
  • PT assumes continuous node and edge activity, which is often unrealistic.

Purpose of the Study:

  • To analyze the impact of node and edge lifetimes on temporal network analysis.
  • To evaluate newly proposed lifetime-preserving reference models (PTN, PTE).
  • To compare spreading dynamics predictions using PT, PTN, and PTE.

Main Methods:

  • Systematic analysis of PT, PTN, and PTE reference models.
  • Characterization of spreading dynamics on synthetic and real-world temporal networks.
  • Evaluation of networks with heterogeneous edge lifespans, burstiness, and dynamic node/edge turnover.

Main Results:

  • Spreading speed predictions vary significantly based on the reference model used.
  • The choice of reference model impacts the characterization of temporal network dynamics.
  • Discrepancies in predictions correlate with the degree of node and edge turnover.

Conclusions:

  • Standard PT models are inadequate for temporal networks with dynamic node/edge activity.
  • Lifetime-preserving models (PTN, PTE) are vital for accurate analysis of spreading dynamics.
  • The study highlights the importance of considering node and edge lifespans in temporal network research.