# Edge-Nodes Representation Neural Machine for Link Prediction
## 4 Experiments and Results
### Results  
From the Table 6 and Figure 8, we can get the conclusion as follows. Compared with other 12 heuristic methods, our proposed method ENRNM achieves unparalleled performance across eight networks with different structures and characteristics in terms of AUC. Our method performs well on almost all the datasets indicating that the method is universal and suitable for almost all kind of networks. Most remarkably, ENRNM performs extremely well on the datasets Power and Router, on which many other heuristic methods' performances are just better than random guessing. For the datasets C.ele and Router, the method PR and RD achieve a bit better performance than our method. However as noticed in Table 6, the methods RD and PR perform poorly on several datasets, they are not universal methods. The method we propose is suitable for almost all the datasets since we gain good effect on all the datasets, although we cannot achieve the best performance on two datasets. In terms of these eight different kinds of datasets, we believe that our method ENRNM is suitable for all kinds of networks and it can achieve enough good performance though may not be the best for some datasets. The red line in Figure 8 indicates the result of our method ENRNM, and we can observe that the red line is the top of almost all the lines.  
Observing the Table 7 and Figure 9, we can conclude that the method Node2 vec performs well on several datasets, and WLNM achieves the best performance on the datasets NS and Router. For the datasets NS and E.coli, the three methods including Node2 vec, WLNM and ENRNM achieve good effects. For six datasets, the method ENRNM has the best performance indicating that we represent the link fully and the neural network learns the most important and meaningful patterns. For dataset  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Datesets** & **CN** & **Jac** & **AA** & **RA** & **PA** & **Katz** & **RD** & **PR** & **SR** & **SBM** & **MF-c** & **MF-r** & **ENRNM** \\ \hline
**Cele** & 0.849 & 0.793 & 0.863 & 0.867 & 0.756 & 0.865 & 0.741 & **0.902** & 0.761 & 0.866 & 0.838 & 0.843 & 0.870 \\
**USAir** & 0.941 & 0.904 & 0.949 & 0.957 & 0.893 & 0.930 & 0.897 & 0.943 & 0.783 & 0.945 & 0.919 & 0.845 & **0.968** \\
**PB** & 0.92 & 0.874 & 0.923 & 0.922 & 0.9012 & 0.927 & 0.882 & 0.934 & 0.772 & 0.937 & 0.933 & 0.942 & **0.9453** \\
**NS** & 0.939 & 0.939 & 0.939 & 0.937 & 0.6823 & 0.941 & 0.583 & 0.941 & 0.941 & 0.921 & 0.638 & 0.721 & **0.9832** \\
**Ecoli** & 0.933 & 0.807 & 0.953 & 0.959 & 0.913 & 0.928 & 0.887 & 0.952 & 0.638 & 0.938 & 0.906 & 0.918 & **0.9787** \\
**Yeast** & 0.892 & 0.891 & 0.892 & 0.893 & 0.825 & 0.922 & 0.881 & 0.926 & 0.915 & 0.915 & 0.839 & 0.883 & **0.9618** \\
**Power** & 0.592 & 0.591 & 0.592 & 0.591 & 0.442 & 0.656 & 0.844 & 0.665 & 0.762 & 0.664 & 0.522 & 0.515 & **0.866** \\
**Router** & 0.561 & 0.562 & 0.561 & 0.562 & 0.472 & 0.379 & **0.925** & 0.381 & 0.368 & 0.852 & 0.775 & 0.782 & **0.921** \\ \hline \hline \end{tabular}
\end{table}
Table 6: AUC results of 12 baselines and ENRNM.  
Figure 8: AUC results of 12 baselines and ENRNM.  
Router, the performance of method WLNM is a little better than ours, but the effect of Node2 vec is unsatisfactory, we consider that the node pattern encoding generated by Node2 vec method may be not suitable for Router. All in all, our method achieves quite good performance on diverse networks with different structures and characteristics. The red line in Figure 9 indicates the result of ENRNM, and we can see that the red line is the top of almost all the lines.