A Dual-Channel Architecture Based on GCN and HGCN for Dynamic Link Prediction

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Dynamic link prediction, which aims to infer future edges from historical network structures, is a fundamental task in dynamic network analysis. Traditional models fail to capture high-order information, while existing methods neglect the distinct temporal evolution patterns between low-order and high-order structures, thereby limiting prediction accuracy. To address these issues, we propose DC-GHCN, a dynamic link prediction model based on a dual-channel architecture that integrates Graph Convolutional Network (GCN) and Hypergraph Convolutional Network (HGCN). Firstly, we extract closed motifs from dynamic network snapshots to construct an initial hypergraph, then refine it via nested motif pruning and node weight compensation strategies. Secondly, we design a dual-channel architecture: the GCN channel learns low-order structural features, while the HGCN channel learns high-order structural features. Furthermore, two independent Gated Recurrent Units (GRUs) separately model the temporal evolution of the two channels. Finally, the model employs a gating mechanism to adaptively fuse the dual-channel node representations for link prediction. Experiments on five real-world dynamic network datasets demonstrate that DC-GHCN outperforms baseline models, validating the effectiveness of the proposed model in dynamic link prediction.