Abstract: To address the limitations of existing key node identification in the complex network,such as shallow feature fusion,insufficient dynamic adaptability,and weak differentiation of node significance,this paper proposes a graph convolutional network model based on multi-feature fusion and ranking optimization(MFR-GCN).The model innovatively incorporates deep feature interaction encoding and a learnable contrastive enhancement mechanism.It achieves dynamic and robust key node detection through hierarchical adaptive gating and conditional global information injection.Firstly,eight representative features spanning local attributes,global attributes,positional attributes,and random-walk properties(including the proposed LASPN centrality) are extracted from the network graph.These features are combined with node embeddings to construct feature vectors.Next,the vectors are fed into a hybrid layer integrating graph convolutional network(GCN) and graph attention network(GAT) for deep feature learning,while skip connections aggregate multi-scale information.Finally,an enhanced multi-component loss function－incorporating ranking loss,variance loss,and clustering loss—is designed for model training and optimization.During inference,a contrastive reinforcement layer amplifies the score differences of key nodes to further distinguish them.Validation experiments using the SIR propagation model are conducted on real-world datasets including Cora,Email,and C.elegans.Results demonstrate that compared to traditional methods like Degree Centrality and Betweenness Centrality,the key nodes identified by MFR-GCN achieve a significantly higher average final infection scale(such as exceeding the suboptimal method by approximately 6.22% on the USairport network).This highlights the model's superior global propagation potential and applicability.

Key words: Key node identification, Graph convolutional neural network, Multi-feature fusion, Ranking optimization, Contrast enhancement mechanism, Complex networks