Stability and Robustness of Time-Varying Opinion Dynamics: A Graph-Theoretic Approach

Abstract

We study the stability of opinion dynamics in the time-varying Friedkin-Johnsen (TVFJ) model, which captures both persistent individual biases and adaptive social influence. We introduce two temporal structures, defected temporal graphs (DTGs) and weakly defected temporal graphs (WDTGs), that serve as graph-theoretic certificates linking stubborn influence and temporal connectivity to contraction of the state-transition matrix. Using these tools, we prove asymptotic stability of TVFJ dynamics under infinitely recurring DTGs, exponential stability in semi-periodic defected networks, and asymptotic stability of a trust-based extension under the weaker condition of recurring WDTGs. We also establish boundedness of the omega-limit set, showing that long-run opinions remain within the convex hull of innate beliefs, and characterize the limit set for periodically switching systems via a p-LTI decomposition with the tight bound that the size of the omega-limit set is at most p. Finally, we show that exponential stability persists under bounded perturbations, ensuring robustness in noisy or imperfect networks. These results unify algebraic contraction tests with interpretable graph-based reasoning, providing scalable and resilient tools for analyzing opinion formation in evolving social and human-AI networks.