Adaptive Semantic Evolution-Driven Reinforced Denoising on Dynamic Graphs

Dynamic graph learning relies on capturing complex topological and temporal evolutions. However, most models extract historical memory using a rigid exponential time-decay paradigm, struggling to model long-range dependencies and inducing severe extraction bias. This bias prevents models from reliably assessing incoming interactions, leading to indiscriminate aggregation that easily incorporates noisy links and propagates errors across nodes. To address this, we propose ATD-DyG, an adaptive evolution and reinforcement denoising framework. First, an adaptive semantic evolution kernel dynamically fits evolutionary patterns to reconstruct high-fidelity representations, eliminating temporal extraction bias. Using these representations as reliable decision priors, a reinforcement learning agent evaluates incoming interactions and proactively truncates high-risk noisy links. This prior-guided topological intervention shifts the paradigm from “passive absorption” to “proactive blocking”, effectively halting noise propagation and enhancing model robustness. Experiments on 7 benchmark datasets demonstrate that ATD-DyG achieves advanced predictive performance and remarkable noise resistance.