Dual-Channel Heterogeneous Graph Neural Network for Automatic Algorithm Recommendation

Automatic algorithm selection is a critical challenge in data-driven decision-making due to the proliferation of available algorithms and the diversity of application scenarios, with no universally optimal solution. Traditional methods, including rule-based systems, grid search, and single-modal meta-learning, often struggle with high computational cost, limited generalization, and insufficient modeling of complex dataset-algorithm interactions, particularly under data sparsity or cold-start conditions. To address these issues, we propose a Dual-Channel Heterogeneous Graph Neural Network (DCHGNN) for automatic algorithm recommendation. Datasets and algorithms are represented as nodes in a heterogeneous bipartite graph, with edge weights defined by observed performance. The framework employs two channels, one for encoding the textual descriptions and the other for capturing the meta-features of the dataset. Cross-channel contrastive learning aligns embeddings to improve consistency, and a random forest regressor predicts algorithm performance on unseen datasets. Experiments on 121 datasets and 179 algorithms show that DCHGNN achieves an average relative maximum value of 94.8%, outperforming baselines, with 85% of predictions in the high-confidence range [0.9, 1]. Ablation studies and visualization analyses confirm the contributions of both channels and the contrastive mechanism. Overall, DCHGNN effectively integrates multimodal information, mitigates sparsity and cold-start issues, and provides robust and accurate algorithm recommendations.