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

Junjie  Yin; Guoquan  Yuan; Lei  Wei; Zhen  Zhang; Huiguang  Xu

doi:10.6180/jase.202608_31.026

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

Junjie Yin¹, Guoquan Yuan¹, Lei Wei¹, Zhen Zhang¹, and Huiguang Xu²This email address is being protected from spambots. You need JavaScript enabled to view it.

¹State Grid Jiangsu Electric Power Co., Ltd. Information Telecommunication Branch, Nanjing 210024, China

²Nari-Tech Nanjing Control Systems Ltd., Nanjing 211100, China

Received: August 30, 2025
Accepted: September 30, 2025
Publication Date: February 15, 2026

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202608_31.026

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 single graph 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.

Keywords: Power Distribution Network; Dynamic Region Partitioning; Heterogeneous Graph Embedding; GraphSAGE Aggregation; R-GCN Convolution

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