Impacts of suppressing guide on information spreading

It is quite common that guides are introduced to suppress the information spreading in modern society for different purposes. In this paper, an agent-based model is established to quantitatively analyze the impacts of suppressing guides on information spreading. We find that the spreading threshold depends on the attractiveness of the information and the topology of the social network with no suppressing guides at all. Usually, one would expect that the existence of suppressing guides in the spreading procedure may result in less diffusion of information within the overall network. However, we find that sometimes the opposite is true: the manipulating nodes of suppressing guides may lead to more extensive information spreading when there are audiences with the reversal mind. These results can provide valuable theoretical references to public opinion guidance on various information, e.g., rumor or news spreading.

💡 Research Summary

This paper investigates how “suppressing guides” – nodes deliberately introduced to curb the spread of information – affect diffusion dynamics on complex networks. Using a simple Markovian agent‑based model, each of N nodes can be in state 0 (uninformed) or 1 (informed). When a node is in state 1 it exerts an influence Aₖₘ on each neighbor m; Aₖₘ is positive for ordinary agents and negative for suppressing guides. At each discrete time step the probability that a node switches to state 1 is a piecewise‑linear function σ of the summed incoming influences. The underlying network is a directed random graph with connection probability p; non‑zero link strengths are drawn from a uniform distribution with mean r, which represents the intrinsic attractiveness of the information. A fraction α of nodes are designated as suppressing guides, and all outgoing links of these nodes are multiplied by –1, turning their influence negative.

Three scenarios are examined. (1) With no suppressing guides (α = 0), the system exhibits a classic threshold behavior: if the average link strength r exceeds a critical value r_c ≈ 1/⟨k⟩ (where ⟨k⟩ is the mean degree), the rumor spreads to more than half of the network; otherwise it quickly dies out. Simulations with ⟨k⟩ = 80 give r_c ≈ 0.0125, confirming that highly connected online platforms are vulnerable to rapid rumor outbreaks. (2) Introducing suppressing guides but assuming ordinary users do not react psychologically (no reversal response) yields a second threshold. Keeping r fixed at 0.02 > r_c and increasing α, the authors find a critical guide ratio α_c ≈ 0.19 beyond which the spreading probability drops to zero. This aligns with the analytical relation r = (1 – 2α)/⟨k⟩, which can be rearranged to α_c = (1 – r⟨k⟩)/2. Hence, more attractive information (larger r) or denser networks (larger ⟨k⟩) require a higher proportion of suppressing guides to achieve control. (3) The most novel contribution concerns “reversal psychology”: when suppressing guides are perceived as oppressive, ordinary users may react by increasing their willingness to share, effectively raising the information’s attractiveness. The authors model this by letting r grow monotonically with α via r(α) = 0.02 + exp