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Unifying information propagation models on networks and influence maximization
1Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom.
Physical Review. E
|October 21, 2022
Summary
We present a unified model for information propagation on networks, generalizing existing models with continuous variables and feedback. Our method efficiently solves the influence maximization problem, crucial for viral marketing strategies.
Area of Science:
- Social Network Analysis
- Information Diffusion Models
- Computational Social Science
Background:
- Information propagation on networks is key in social sciences, impacting areas like viral marketing.
- Classical models like independent cascade and linear threshold have limitations.
- Influence maximization is a critical problem with broad applications.
Purpose of the Study:
- To unify and generalize existing information propagation models.
- To formulate and solve the influence maximization problem for this new model.
- To develop efficient algorithms for solving the influence maximization problem.
Main Methods:
- Developed a unified model with continuous variables and feedback.
- Formulated influence maximization as a mixed integer nonlinear programming problem.
- Employed derivative-free optimization methods and specialized direct search algorithms.
Main Results:
- The influence maximization problem is exactly solvable for linear dynamics, linked to Katz centrality.
- A customized direct search method demonstrates near-optimal performance.
- Numerical validation on synthetic and real-world networks confirms method efficacy.
Conclusions:
- The unified model provides a more comprehensive framework for studying information propagation.
- The proposed algorithms offer efficient solutions for influence maximization.
- This work advances understanding and application of network influence.
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