Hybrid-GNN: Dynamic Regional Reconstruction and Topology Verification of Main Distribution Network by Integrating GraphSAGE and R-GCN

Traditional distribution network region reconstruction techniques struggle to cope with frequent changes in network topology and dynamic equipment adjustments, leading to segmentation delays and errors. This paper proposes a novel hybrid graph neural network (Hybrid-GNN) framework that integrates the neighborhood sampling and aggregation mechanism of GraphSAGE with the multi-relation convolution mechanism of R-GCN to simultaneously address the dual tasks of dynamic region reconstruction and topology verification. This model constructs a heterogeneous graph containing real-time attributes and multiple electrical connection types and adapts to dynamic changes using scalable embedding fusion techniques. Through neighborhood sampling and relation-sensitive convolution, node representations are gradually refined, achieving real-time and accurate region segmentation and topology verification. Experiments demonstrate that the Hybrid-GNN maintains high node consistency ( 90.5% ) under 30% topological perturbation, achieves anomaly localization accuracy of 91.2%, and efficiently processes networks with tens of thousands of nodes (within 1200 ms ) in edge environments, significantly outperforming traditional methods and singlegraph models. Its innovation lies in the simultaneous modeling of structural connectivity and multi-relation semantics, combined with scalable fusion techniques to adapt to dynamic grid conditions, providing important support for smart distribution systems.