# SR-GNN: A Signed Network Embedding Method Guided by Status Theory and a Reciprocity Relationship
## 4 Experimental Evaluation
### Experiment Results
#### 4.2.3 Sign Prediction
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Metrics**} & \multicolumn{3}{c}{**Undirected Model**} & \multicolumn{3}{c}{**Directed Model**} & \\ \cline{3-10}  & & **SGCN** & **SNEA** & **SDGNN** & **S-GNN** & **BESIDE** & **SIDE** & **SR-GNN** \\ \hline \multirow{4}{*}{Epinions} & AUC & 0.8445 & 0.8617 & 0.9408 & 0.9194 & 0.9348 & 0.8768 & **0.9425** \\  & Binary-F1 & 0.9206 & 0.9239 & 0.9628 & 0.9448 & 0.9611 & 0.9487 & **0.9633** \\  & Micro-F1 & 0.9071 & 0.8749 & 0.9355 & 0.9112 & 0.9339 & 0.9092 & **0.9370** \\  & Macro-F1 & 0.8105 & 0.7862 & 0.8610 & 0.8254 & 0.8601 & 0.7217 & **0.8697** \\ \hline \multirow{4}{*}{Slashdot} & AUC & 0.7863 & 0.8032 & 0.8921 & 0.8944 & **0.9017** & 0.7180 & _0.8961_ \\  & Binary-F1 & 0.8596 & 0.8804 & 0.9128 & 0.9125 & **0.9142** & 0.8675 & _0.9132_ \\  & Micro-F1 & 0.8296 & 0.8238 & 0.8606 & _0.8617_ & 0.8589 & 0.7728 & **0.8622** \\  & Macro-F1 & 0.7403 & 0.7723 & 0.7892 & _0.7893_ & 0.7885 & 0.5860 & **0.7943** \\ \hline \multirow{4}{*}{WikiRfA} & AUC & 0.8563 & 0.7769 & 0.8898 & _0.8960_ & **0.8979** & 0.6824 & 0.8932 \\  & Binary-F1 & 0.9069 & 0.8614 & **0.9142** & 0.9130 & 0.9097 & 0.8748 & _0.9135_ \\  & Micro-F1 & 0.8489 & 0.7967 & **0.8627** & _0.8613_ & 0.8553 & 0.7793 & 0.8603 \\  & Macro-F1 & 0.7527 & 0.7403 & **0.7849** & 0.7820 & 0.7723 & 0.5713 & _0.7790_ \\ \hline \multirow{4}{*}{Bitcoin-OTC} & AUC & 0.8455 & 0.8046 & **0.9124** & _0.8970_ & 0.8629 & 0.7881 & 0.8729 \\  & Binary-F1 & 0.9418 & 0.9083 & **0.9647** & 0.9570 & 0.9523 & 0.9023 & _0.9598_ \\ \cline{1-1}  & Micro-F1 & 0.8978 & 0.8875 & **0.9357** & _0.9273_ & 0.9230 & 0.8821 & 0.9220 \\ \cline{1-1}  & Macro-F1 & 0.7266 & 0.7311 & **0.8017** & _0.7908_ & 0.7682 & 0.7301 & 0.7700 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sign prediction results. impact of network properties, such as the number of reciprocal edges and the distribution of reciprocal edges, on prediction outcomes. SDGNN performs well but incurs high computational costs. It defines four types of motifs with various edge directions and signs, using attention methods for information propagation and aggregation. This approach achieved the best results on the WikiRfA and Bitcoin-OTC datasets and performed competitively on two other datasets. However, due to the high complexity of the objective function, the runtime exceeded 8 h on the medium-sized WikiRfA dataset (11,258 nodes). S-GNN is competitive, and it is based on status theory; it propagates and aggregates information in both sending and receiving directions. The objective function relates node representations to their status scores, using multi-layer convolution to capture higher-order neighborhood information, performing better than undirected signed network models. Since SIDE is a shallow model based on random walks, it is slightly less effective. BESIDE achieved good results in some situations, showing that a sort of \"bridge\" node, which cannot form triangles, is particularly significant. As indicated in Table 1, a significant proportion of nodes in all four networks could not form triangles, and further research is needed to combine the attributes of such nodes with status theory and structural balance theory.