Tighter bounds improve control of information flow in social networks
A Novel Approach for the SDIR Epidemic Model on Online Social Networks
Social and Information NetworksNetworking and Internet Architecture
Summary
Controlling how information spreads online can be done by removing certain links between people. The authors found that previous methods to pick which links to cut were not accurate enough for a new model called SDIR. They created a better way to analyze the spread, using a more detailed mathematical approach. Their method performs better in tests on both made-up and real social networks, helping to more effectively limit the spread of information.
What this means in practice
- •For social media platform engineers: Identify and remove key user connections to slow down misinformation spread more effectively in online social networks using improved diffusion bounds.
- •For public health data analysts: Use refined network link analysis to better simulate and manage information or disease spreading in community contact networks.
Authors
Nguyen Hong Phuc, Duong Khanh Ly, Hoang Phi Dung
Abstract
Information diffusion can be controlled by restricting or removing links (edges) in online social networks, as well as in real-world networks. To identify the most influential links to remove while minimizing diffusion, previous studies have proposed upper bounds for spreading processes in SIR and SIS models, using supermodularity and weighted matrices to identify critical links in contact networks. However, in some cases, existing upper bounds are not sufficiently tight to accurately capture the effect of important edges, as in the SDIR model of [14] (Khanh-Cho-Dung, Proceedings of 40th ICOIN, 2026). We therefore propose a tighter upper bound for controlling diffusion in the SDIR model by directly analyzing the dynamics of the two state vectors D and I in a $2N$-dimensional space. This approach yields an improved spectral-radius convergence condition and outperforms the previous method. Simulations on the synthetic Erdos-Renyi network and the real-world Haslemere dataset using a Greedy edge-deletion algorithm demonstrate its effectiveness for influence minimization on social networks.