Knowledge-Driven Virtual Network Embedding with Dynamic World Model

Virtual Network Embedding (VNE) is a fundamental yet challenging task in network virtualization, particularly in dynamic and uncertain environments. While deep reinforcement learning (DRL) has shown promise, its reliance on implicit knowledge modeling—entangling historical experience within neural network parameters—constrains adaptability and interpretability, hindering deployment in production networks. To address this, we introduce Knowledge-Driven VNE (KD-VNE), a framework built on the principle of decoupling knowledge representation from the policy network. At its core, KD-VNE leverages a Temporal Knowledge Graph (TKG) as an explicit world model, empowering the agent with structured reasoning over network dynamics. This externalized knowledge is integrated within a hybrid Actor-Critic agent that synthesizes real-time state, TKG-curated experience, and domain heuristics through a dual-loop mechanism, enabling both rapid tactical adaptation and long-term strategic optimization. Extensive experiments show that KD-VNE achieves competitive acceptance ratios and improved economic efficiency. More critically, it demonstrates exceptional cross-domain generalization, achieving the highest acceptance ratio on real-world network topologies—outperforming the DRL baseline by over 80%—all without retraining.