Abstract：A graph convolutional network model based on multi-scale feature fusion is proposed to address the key node identification problem in power communication networks. This model combines the inception architecture, deformable convolution, and depth-separable convolution to capture the network's node features from various scales, thereby improving the model's ability to characterize the network's complex features. In the meantime, the model's capacity to recognize the intricate details of the power communication network is enhanced by its ability to adaptively learn and assign the weights of various structural qualities during the training phase thanks to the construction of multi-channel inputs. Experiments reveal that the model outperforms standard algorithms in power communication network key node identification applications, exhibiting good performance in accuracy and robustness.
何玉钧, 李璇, 曹竟妍. 基于MFF-GCN的电力通信网络关键节点识别研究[J]. 复杂系统与复杂性科学, 2026, 23(3): 45-52.
HE Yujun, LI Xuan, CAO Jingyan. Key Node Identification of Power Communication Network Based on MFF-GCN[J]. Complex Systems and Complexity Science, 2026, 23(3): 45-52.
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