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Scalable Influence Estimation in Continuous-Time Diffusion Networks.

Nan Du1, Le Song1, Manuel Gomez-Rodriguez2

  • 1Georgia Institute of Technology.

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Summary
This summary is machine-generated.

We developed a randomized algorithm to predict information spread in large online networks. This scalable method accurately estimates influence, improving content virality prediction and selection for maximum reach.

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Area of Science:

  • Computer Science
  • Network Science
  • Data Science

Background:

  • Predicting information spread on the internet is challenging due to real-time dynamics and network scale.
  • Estimating the influence of content or users is crucial for understanding virality and optimizing dissemination.

Purpose of the Study:

  • To propose a scalable and accurate randomized algorithm for influence estimation in continuous-time diffusion networks.
  • To enhance influence maximization strategies by providing a reliable subroutine for influence assessment.

Main Methods:

  • A novel randomized algorithm for influence estimation in continuous-time diffusion networks.
  • Analysis of computational complexity and accuracy guarantees.
  • Integration of the algorithm into a greedy influence maximization framework.

Main Results:

  • The algorithm achieves high accuracy (ε) with O(1/ε^2) randomizations and efficient computation.
  • Guaranteed performance for influence maximization, achieving at least (1 - 1/e) OPT - 2C.
  • Demonstrated scalability to networks with millions of nodes on synthetic and real-world data.

Conclusions:

  • The proposed randomized algorithm offers a scalable and accurate solution for influence estimation in large networks.
  • It significantly outperforms existing methods in accuracy and the quality of selected nodes for influence maximization.
  • This work advances the ability to predict and optimize information diffusion online.