# A Combinatory Framework for Link Prediction in Complex Networks
## 8 Evaluation
### EML Network Dataset
\begin{table}
\begin{tabular}{l c|c c|c c} \hline \hline \multicolumn{8}{c}{**EML Comparable Tests**} \\ \hline \multicolumn{8}{c}{**Test 1: Composition of adam_adar** - cnc - res_alloc - **jacc** - pref_att vs. adam_adar} \\ \hline \multirow{2}{*}{_Subgraph_} & \multicolumn{2}{c}{_Predictor Score_} & \multicolumn{2}{c}{_Best Basic Algorithm Score_} \\  & _/AUC/Precision_ & _/AUC/Precision_ & _Difference_ \\ \hline Subgraph\_0 (train set) & 341 & 0.947 & 0.215 & 334 & 0.824 & 0.211 & +7 more \\ \hline Subgraph\_1 (test set) & 310 & 0.948 & 0.204 & 305 & 0.827 & 0.200 & +5 more \\ \hline Subgraph\_2 (test set) & 320 & 0.944 & 0.211 & 320 & 0.834 & 0.211 & +0 more \\ \hline
**Test 2: Composition of res_alloc - cnc vs. res_alloc** & & & & & \\ \hline \multirow{2}{*}{_Subgraph_} & \multicolumn{2}{c}{_Predictor Score_} & \multicolumn{2}{c}{_Best Basic Algorithm Score_} & \multirow{2}{*}{_Difference_} \\  & _/AUC/Precision_ & _/AUC/Precision_ & & & & \\ \hline Subgraph\_0 (train set) & 341 & 0.926 & 0.217 & 297 & 0.814 & 0.187 & +4 more \\ \hline Subgraph\_1 (test set) & 311 & 0.925 & 0.204 & 278 & 0.826 & 0.183 & +33 more \\ \hline Subgraph\_2 (test set) & 320 & 0.926 & 0.211 & 282 & 0.819 & 0.186 & +38 more \\ \hline
**Test 3: Composition of adam_adar** - cnc - res_alloc - **jacc** - pref_att vs. adam_adar** & & & & \\ \hline \multirow{2}{*}{_Subgraph_} & \multicolumn{2}{c}{_Predictor Score_} & \multicolumn{2}{c}{_Best Basic Algorithm Score_} & \multirow{2}{*}{_Difference_} \\  & _/AUC/Precision_ & & & & & \\ \hline Subgraph\_0 (test set) & 334 & 0.948 & 0.211 & 334 & 0.824 & 0.211 & +0 more \\ \hline Subgraph\_1 (train set) & 314 & 0.945 & 0.206 & 305 & 0.827 & 0.200 & +9 more \\ \hline Subgraph\_2 (test set) & 323 & 0.939 & 0.213 & 320 & 0.834 & 0.211 & +3 more \\ \hline Subgraph\_2 (test set) & _Predictor Score_ & _Best Basic Algorithm Score_ & & & \\ \hline \multirow{2}{*}{_Subgraph_} & \multicolumn{2}{c}{_(_AUC/Precision_} & \multirow{2}{*}{_AUC/Precision_} & \multirow{2}{*}{_Difference_} \\  & _/AUC/Precision_ & & & & & \\ \hline Subgraph\_0 (test set) & 332 & 0.909 & 0.209 & 297 & 0.814 & 0.187 & +35 more \\ \hline Subgraph\_1 (train set) & 312 & 0.929 & 0.205 & 278 & 0.826 & 0.183 & +34 more \\ \hline Subgraph\_2 (test set) & 321 & 0.921 & 0.211 & 282 & 0.819 & 0.186 & +39 more \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparable tests in EML network. examining. The more suitable the algorithm is for the network, the more these edges will be. However, from one position in the evaluation list onwards, edges appear which, although they have high estimate values, will not be added to the graph in the near future. This makes sense because the basic algorithm makes predictions according to the logic with which it was written. These predictions cannot be perfect precisely because they are predictions. The framework and the enhanced predictor tool we propose tries to combine as best as possible the separate results of each basic algorithm through its evaluation list. So we are interested in each basic algorithm, in addition to the common edges it will have in its evaluation list with the evaluation lists of the other basic algorithms, to also contain different edges so that they have enough probabilities to appear in the evaluation list of our enhanced predictor tool that composes them. And this is actually accomplished when we compose in the enhanced predictor tool only two basic algorithms in Tests 2 and 4. When there are many basic algorithms for composition, then together with the correct selections of the edges, the incorrect selections which obtain a high score multiply. This results in our enhanced predictor tool's evaluation list containing more wrong basic algorithm edge choices than we would like. Ideally, we want to use basic algorithms that make good predictions for the network they are applied to, that are as diverse as possible in their evaluation list, but that are also few in number.