# Link Prediction with Hypergraphs via Network Embedding
## 4 Conclusions and Discussions  
In this paper, a link prediction approach with network embedding was proposed for hypergraphs. The proposed HNE method applied the Deepwalk model to extract features of nodes according to the hypergraphs constructed from library loan records, then a classifier was trained to predict the potential links between nodes. The experiment results on seven datasets showed that our approach outperformed typical link prediction methods. The comparison of AUC, precision, recall and F1-score with six methods demonstrated the effectiveness of the proposed approach. In the future, the idea of combining hypergraphs and network embedding can not only be applied to link prediction, but also implement more tasks, such as node importance, community detection and node classification. In addition, our proposed algorithm has wide practical applications, such as recommendations for online social networks, knowledge reasoning for knowledge hypergraph construction, drug-target prediction or drug-disease prediction in the field of bioinformatics and so on. In addition, as more graph neural network methods [44, 45, 46, 47] are proposed, we can explore hyperlink prediction algorithms and other graph neural network models for preserving more structural and semantic information of hypergraphs to solve the fundamental problems in hypergraph analysis. Conceptualization, Z.Z. and K.Y. ; methodology, K.Y. ; software, Z.Z. ; validation, Z.Z., K.Y. and J.G. ; writing--original draft preparation, Z.Z. ; writing--review and editing, Z.Z., K.Y. and J.G. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (No. 71571119), and the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 22 KJD120002). **Institutional Review Board Statement**: Not applicable. **Data Availability Statement**: Our datasets could be downloaded in [[http://hdl.handle.net/20.500.1](http://hdl.handle.net/20.500.1)]([http://hdl.handle.net/20.500.1](http://hdl.handle.net/20.500.1)) 2291/10022 (accessed on 8 October 2022). **Conflicts of Interest**: The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**SHOU**} & \multicolumn{3}{c}{**SUFE**} & \multicolumn{3}{c}{**SUEP**} \\ \cline{2-11}  & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline CN & 0.6971 & 0.6091 & 0.6499 & 0.8996 & 0.6654 & 0.7650 & 0.7063 & 0.5645 & 0.6275 \\ Jaccard & 0.7569 & 0.6034 & 0.6715 & 0.9034 & 0.6916 & 0.7834 & 0.7412 & 0.5424 & 0.6263 \\ Katz & 0.6705 & 0.8001 & 0.7333 & 0.6722 & 0.8121 & 0.7356 & 0.6663 & 0.8061 & 0.7296 \\ RWR & 0.5446 & 0.5456 & 0.5449 & 0.5929 & 0.6250 & 0.6083 & 0.5328 & 0.5366 & 0.5337 \\ Node2 vec & 0.8317 & 0.8037 & 0.8223 & 0.9416 & 0.8566 & 0.898 & 0.8401 & 0.8154 & 0.8275 \\ GCN & 0.7959 & 0.7675 & 0.7814 & 0.9046 & 0.8372 & 0.8696 & 0.7934 & 0.7734 & 0.7832 \\ HNE & 0.8379 & 0.8052 & 0.8212 & 0.9424 & 0.8685 & 0.9040 & 0.8516 & 0.8331 & 0.8422 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The experimental results for the precision, recall and F1-Score on the SHOU, SUFE, SUEP.