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Area of Science:

  • Network Science
  • Computational Social Science
  • Machine Learning

Background:

  • Current information diffusion models in online social networks often rely on deterministic graphs, which are too restrictive for real-world scenarios.
  • Online social network behaviors are inherently uncertain, unpredictable, and time-varying, necessitating more flexible modeling approaches.
  • Stochastic graphs, where link weights are random variables, offer a promising alternative for modeling such complex network dynamics.

Purpose of the Study:

  • To propose a novel information diffusion model based on stochastic graphs with unknown influence probabilities.
  • To develop an effective method for estimating these unknown influence probabilities within the stochastic diffusion model.
  • To demonstrate the utility of the proposed model for the critical problem of influence maximization in online social networks.

Main Methods:

  • Developed a diffusion model utilizing stochastic graphs where influence probabilities are treated as unknown random variables.
  • Employed a set of learning automata within the diffusion model to estimate influence probabilities through link sampling.
  • Conducted numerical simulations on both real and artificial stochastic networks to validate the model's performance.

Main Results:

  • The proposed stochastic diffusion model effectively estimates unknown influence probabilities in online social networks.
  • The model demonstrates significant improvements in influence maximization compared to traditional deterministic approaches.
  • Simulations confirmed the model's robustness and effectiveness across various network types.

Conclusions:

  • Stochastic graphs provide a more realistic and powerful framework for modeling information diffusion in online social networks.
  • The learning automata-based approach offers an efficient method for parameter estimation in stochastic network models.
  • This research advances the field of influence maximization by providing a more accurate and adaptable modeling solution.