GNN-Based Method for Data Flow Path Completion in Electric Power Information Networks

The reliability of power information networks is paramount for smart grid security, yet incomplete data transmission paths due to link failures or cyberattacks critically impair topology awareness and operational decision-making. To address the limitations of existing methods in handling the dynamic and structurally complex nature of these networks, this paper proposes a Topology-Enhanced Dynamic Perception Graph Neural Network (TEDP-GNN) for accurate path completion. The core methodology revolves around three integrated innovations. First, we introduce a topological deep learning framework that models high-order node interactions and network connectivity patterns beyond pairwise relationships. This is achieved by employing simplicial complexes to capture multi-node dependencies inherent in power data routing structures. Second, a novel dynamic perception mechanism is designed to continuously monitor link state changes. This mechanism utilizes a temporal gating unit that ingests real-time network alert data, enabling the model to adaptively reweight message-passing pathways in response to topological disturbances such as failures or attacks. Finally, the task is formulated as a topology-constrained optimization problem. Within this framework, we deploy a hybrid attention module that simultaneously computes both node-level and path-level attentions, alongside a multi-dimensional feature aggregation strategy that synthesizes information from topological embeddings, dynamic states, and historical transmission patterns. Preliminary validation on simulated power communication topologies demonstrates that TEDP-GNN significantly outperforms baseline models in predicting missing links, showing marked improvements in precision and recall. The model provides a robust, topology-aware solution for maintaining data path integrity, thereby enhancing the situational awareness and resilience of power information infrastructures.