## 3 Results
### Results on Link Prediction  
Figure 4 and Table 2 present the performance comparison of HMAGNN with various baseline methods on multiple datasets, including those from the Open Graph Benchmark (OGB) and the Jujube dataset, in the context of link prediction. Methods leveraging closed subgraphs like SEAL, Neo, and HMAGNN consistently outperform traditional heuristic methods, emphasizing the efficacy of learned heuristics over manually designed ones. Across datasets, GNN-based methods generally outshine traditional heuristic methods, highlighting the ability of GNNs to automatically capture node and edge feature representations. Notably, HMAGNN exhibits superior performance among the GNN-based methods, showcasing its effectiveness in capturing intricate relationships and structural features. On the OGB-PPA dataset, feature-based GNNs demonstrate suboptimal performance, suggesting that these methods might not fully exploit structural information. In contrast, SEAL, Neo, and HMAGNN, which incorporate structural information, outperform GCN and GraphSAGE. Though SEAL achieves strong performance overall, it faces challenges on the OGB-DDI dataset, indicating potential issues in integrating input node and structural features for this specific dataset. HMAGNN performs exceptionally well on the Jujube dataset, surpassing other methods. However, there is a noticeable decrease in prediction accuracy across all models on the Jujube dataset compared to other OGB datasets. This decline may be attributed to the smaller size of the Jujube dataset, limiting the amount of available training data. HMAGNN demonstrates adaptability by combining effectively with GCN, showcasing improved performance. The collaborative utilization of both methods contributes to enhanced link prediction accuracy. The results highlight HMAGNN's robust performance, particularly in scenarios involving closed subgraphs and datasets with complex structural information. Further investigations, possibly through statistical significance tests, could enrich understanding of the observed differences in model performances.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **PPA** & **DDI** & **Collab** & **Citation2** & **Jujube** \\ \hline CN & 27.65 \(\pm\) 0.00 & 17.73 \(\pm\) 0.00 & 50.06 \(\pm\) 0.00 & 76.20 \(\pm\) 0.00 & 18.97 \(\pm\) 0.00 \\ AA & 32.45 \(\pm\) 0.00 & 18.61 \(\pm\) 0.00 & 53.00 \(\pm\) 0.00 & 76.12 \(\pm\) 0.00 & 21.19 \(\pm\) 0.00 \\ RA & 49.33 \(\pm\) 0.00 & 6.23 \(\pm\) 0.00 & 52.89 \(\pm\) 0.00 & 76.20 \(\pm\) 0.00 & 19.88 \(\pm\) 0.00 \\ MF & 32.29 \(\pm\) 0.00 & 33.70 \(\pm\) 0.03 & 48.96 \(\pm\) 0.00 & 51.89 \(\pm\) 0.04 & 37.46 \(\pm\) 0.02 \\ MLP & 0.47 \(\pm\) 0.05 & — & 19.98 \(\pm\) 0.96 & 28.99 \(\pm\) 0.16 & 22.47 \(\pm\) 0.11 \\ Node2 Vec & 17.24 \(\pm\) 0.76 & 21.95 \(\pm\) 1.58 & 41.36 \(\pm\) 0.69 & 53.47 \(\pm\) 0.12 & 22.13 \(\pm\) 0.86 \\ GCN & 16.98 \(\pm\) 1.33 & 44.60 \(\pm\) 8.87 & 47.01 \(\pm\) 0.79 & 84.79 \(\pm\) 0.24 & 43.17 \(\pm\) 4.38 \\ GraphSAGE & 13.93 \(\pm\) 2.38 & 48.01 \(\pm\) 9.02 & 48.60 \(\pm\) 0.46 & 82.62 \(\pm\) 0.01 & 53.61 \(\pm\) 5.46 \\ SEAL & 48.15 \(\pm\) 4.17 & 26.25 \(\pm\) 6.00 & 54.37 \(\pm\) 0.02 & 86.32 \(\pm\) 0.52 & 62.78 \(\pm\) 0.43 \\ Neo & 49.13 \(\pm\) 0.60 & 63.57 \(\pm\) 3.52 & 57.52 \(\pm\) 0.37 & **87.26 \(\pm\) 0.84** & 63.61 \(\pm\) 0.77 \\ HMAGNN & **49.72 \(\pm\) 3.06** & **64.66 \(\pm\) 7.74** & **57.63 \(\pm\) 0.72** & 86.54 \(\pm\) 0.34 & **65.28 \(\pm\) 0.22** \\ \hline \end{tabular}
\end{table}
Table 2: Link prediction performance of HMAGNN and baselines on different datasets. Bold indicates the best performance.