Jul 2026· Frontiers in Computing and Intelligent Systems· Vol 17, pp. 31-36· 0 citations· 14 references
TL;DR
A sparse optimal control framework built on the Network Drift-Diffusion Model (NDDM) is proposed, which shows that the best centrality choice depends strongly on the network topology, and the system undergoes phase transitions as control parameters vary.
Abstract
This paper studies the problem of steering collective beliefs in social networks when only a small fraction of nodes can be directly influenced. We propose a sparse optimal control framework built on the Network Drift-Diffusion Model (NDDM). Two intervention mechanisms are considered: direct control and latent (indirect) control. To select which nodes to actuate, we compare six centrality measures---Degree, Betweenness, Eigenvector, Closeness, PageRank, and K-shell---and keep only the top 5\%--30\% as control inputs. The optimal feedback law follows from the Hamilton-Jacobi-Bellman (HJB) equation, which reduces to solving Riccati-type differential equations. We test our approach on Erdős-Rényi (ER), Barabási-Albert (BA), and Watts-Strogatz (WS) networks. The results show that the best centrality choice depends strongly on the network topology, and the system undergoes phase transitions as control parameters vary.
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 sup...
Consensus-building can be considered a cornerstone of democratic societies and effective governance. Recently, it has been shown that social contagions can shape some forms of consensus-based collective decision-making
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. The topology of the network underpinning such processes plays a key role in promoting or ha...
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