Identifying hot-spot pathways in fishery science and technology innovation through temporal heterogeneous graph neural networks.

Accurately identifying "hot-spot pathways" in fishery science and technology (S&T) innovation is critical for food security, economic development, and ecological sustainability. Traditional technology foresight methods struggle to capture complex, dynamic evolutionary patterns in S&T innovation networks. Drawing on Dosi's conceptual framework of technological trajectories as domain-inspired design heuristics-whereby Dosi's qualitative concepts provide structural guidance for model design rather than formal axioms that exhaustively capture the theoretical framework-this study proposes DTH-GNN (Documents-based Temporal Heterogeneous Graph Neural Network), integrating Graph Neural Networks with dynamic evolutionary analysis to identify potential hot-spot pathways. We construct a dynamic heterogeneous knowledge graph from multi-source data (2010-2024) encompassing 32,847 publications, 8,956 patents, and 1,856 projects. DTH-GNN combines an R-GCN-based heterogeneous encoder with a GRU-based temporal evolution module, achieving AUC = 0.934 and AP = 0.928 (after rigorous leakage assessment), significantly outperforming GCN, R-GCN, and EvolveGCN baselines. Information-theoretic analysis indicates that temporal features account for a substantial share of mutual information in link prediction (31.8%, 95% CI: [28.4%, 35.1%]), comparable to structural features (29.1%) and higher than attribute features (19.7%). Three high-potential pathways are identified and validated through expert evaluation (Krippendorffś α = 0.804): Smart Aquaculture, Green Seed Industry, and Ecological Fisheries. These findings provide data-driven scientific support for S&T investment prioritization in the fishery sector.