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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Influence maximization on temporal networks.

Şirag Erkol1, Dario Mazzilli1, Filippo Radicchi1

  • 1Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana 47408, USA.

Physical Review. E
|November 20, 2020
PubMed
Summary
This summary is machine-generated.

Optimizing influence maximization on temporal networks requires understanding network dynamics. Early network evolution knowledge, even partial, is key to identifying effective spreaders for maximizing outbreak size.

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Area of Science:

  • Network Science
  • Computational Epidemiology
  • Complex Systems

Background:

  • Spreading processes on networks are crucial for understanding phenomena like disease outbreaks and information diffusion.
  • Temporal networks, which capture the time-varying nature of interactions, offer a more realistic model for these processes.
  • The influence maximization problem aims to select initial nodes (seeds) to maximize the spread of influence.

Purpose of the Study:

  • To investigate the optimization problem of seeding spreading processes on temporal networks to maximize expected outbreak size.
  • To evaluate the impact of different levels of network information on influence maximization effectiveness.
  • To identify essential information for effectively solving the influence maximization problem in dynamic network settings.

Main Methods:

  • Framing the problem using the susceptible-infected-recovered (SIR) model on temporal networks.
  • Conducting a systematic analysis using a corpus of 12 real-world temporal networks.
  • Quantifying the performance of influence maximization solutions based on varying network information.

Main Results:

  • Static or aggregated network topology information is insufficient for effective influence maximization.
  • Partial knowledge of early network dynamics is crucial for identifying near-optimal seed sets.
  • Dynamic information about network evolution significantly outperforms static approaches.

Conclusions:

  • Effective influence maximization on temporal networks necessitates understanding their dynamic nature.
  • Focusing on early-stage network dynamics provides essential insights for selecting influential spreaders.
  • Future strategies should incorporate temporal network dynamics for improved seeding in spreading processes.