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Program Objectives
Vision
Problem Statement
Impact
Information spreads through social networks in ways shaped by both individual behavior and network topology. Classical epidemic and diffusion models (e.g., SIS, Bass) explain basic spreading dynamics but often assume either irreversible adoption or fully reversible behavior, which cannot capture real scenarios such as temporary adoption, misinformation correction, or trend decline. We aim to understand how individuals move between adopting, rejecting, and re-adopting information, and how network structure—especially the role of highly connected nodes—governs the speed, extent, and stabilization of information diffusion.
We propose a unified diffusion framework that extends epidemic and innovation models by incorporating a reversible intermediate adoption state, allowing individuals to temporarily adopt and later either revert or commit permanently. By simulating this model across different network structures (random, small-world, and scale-free), we aim to characterize how social structure shapes diffusion dynamics. Our vision is to capture realistic information behaviors—rising, fluctuating, and stabilizing—rather than assuming monotonic spread.
This project will formalize a three-state diffusion model (neutral, susceptible, adopted) with both reversible and irreversible transitions. We will derive transition rules based on network connectivity and behavioral parameters, then simulate diffusion under different network topologies and seeding strategies. Key outcomes include understanding how topology and centrality influence adoption speed, peak diffusion timing, long-run adoption fractions, and the balance between temporary versus permanent adoption.
This model enables a richer understanding of social contagion and can inform settings such as misinformation mitigation, viral marketing, and public health messaging. By analyzing how network structure and influential nodes affect diffusion persistence, decay, and saturation, the project contributes to behavioral network theory and provides insights for strategies that encourage or suppress information spread in real systems
Network Analysis of Information Diffusion in Social Interaction Systems
Yingnan Li，Ziqi Wang
Reference List
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1.Li, M.; Wang, X.; Gao, K.; Zhang, S. A Survey on Information Diffusion in Online Social Networks: Models and Methods. Information 2017, 8, 118. https://doi.org/10.3390/info8040118
2.Y. Jiang and J. C. Jiang, "Diffusion in Social Networks: A Multiagent Perspective," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 45, no. 2, pp. 198-213, Feb. 2015, doi: 10.1109/TSMC.2014.2339198.
3.Kumar, P., Sinha, A. Information diffusion modeling and analysis for socially interacting networks. Soc. Netw. Anal. Min. 11, 11 (2021). https://doi.org/10.1007/s13278-020-00719-7
4.Yagan O, Qian D, Zhang J, Cochran D (2012) Information diffusion in overlaying social-physical networks. In: IEEE annual conference on information sciences and systems (CISS), Princeton NJ, pp 1–6, March 2012