# SR-GNN: A Signed Network Embedding Method Guided by Status Theory and a Reciprocity Relationship
## 4 Experimental Evaluation
### Experiment Results  
#### 4.2.1 Baselines  
In order to demonstrate the efficiency of the SR-GNN model, we have conducted a comparison with several cutting-edge algorithms that have been recently proposed. Table 2 lists the technical characteristics of each algorithm. Since there are few algorithms for processing directed signed networks, we introduced two classic algorithms for undirected signed networks and two shallow learning models for directed signed networks in the benchmark:  
1. SGCN (available online: [[https://github.com/benedekrozemberczki/SGCN](https://github.com/benedekrozemberczki/SGCN)]([https://github.com/benedekrozemberczki/SGCN](https://github.com/benedekrozemberczki/SGCN)), accessed on 20 November 2024): The first GNN-based model for signed network [4] modeling introduced in the literature was by [PERSON] et al. in 2018. It independently aggregates and propagates friends and foes based on structural balance theory using a complex aggregation and propagation technique. 2. SNEA (available online: [[https://github.com/liyu1990/snea](https://github.com/liyu1990/snea)]([https://github.com/liyu1990/snea](https://github.com/liyu1990/snea)), accessed on 20 November 2024): A GNN for undirected signed networks based on structural balance theory [5]. It designed a masked self-attentional layer, which used a self-attention mechanism to estimate the importance coefficient for pairs of nodes connected by different types of links during the embedding aggregation process. This model's aggregation and propagation strategies are inspired by the SGCN model. 3. SDGNN (available online: [[https://github.com/huangjunjie-cs/SIGAT](https://github.com/huangjunjie-cs/SIGAT)]([https://github.com/huangjunjie-cs/SIGAT](https://github.com/huangjunjie-cs/SIGAT)), accessed on 20 November 2024): The first GNN model based on directed signed networks, to the best of our knowledge [20]. SDGNN utilizes four directed signed relationships, \(e_{i\to j}^{+}\), \(e_{i\to j}^{-}\), \(e_{i\to j}^{+}\), and \(e_{i\to j}^{-}\), to design a GNN aggregator. It provides an attention-based GNN aggregator to collect information from four different types of edges and employs a supervised learning loss function to rebuild signs, directions, and triadic closure. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Node** & **Edge** & **+Edge \%** & **–Edge \%** & **Reciprocal** & **Reciprocal** & **Non-Triadic** \\  & & & & & **+Edge \%** & **–Edge \%** & **Closure \%** \\ \hline Epinions & 131828 & 841372 & 85.3\% & 14.7\% & 29.7\% & 0.6\% & 20.2\% \\ Slashdot & 82140 & 579919 & 78.6\% & 21.4\% & 15.4\% & 1.7\% & 44.8\% \\ WikiRfa & 11258 & 185627 & 77.8\% & 22.2\% & 6.3\% & 0.2\% & 45.3\% \\ Bitcoin-OTC & 5881 & 35592 & 90.0\% & 10.0\% & 75.5\% & 1.7\% & 35.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistics of the datasets. S-GNN (available online: [[https://www.dropbox.com/scl/fo/47m08](https://www.dropbox.com/scl/fo/47m08) tp7g3 dlz6 irfn2]([https://www.dropbox.com/scl/fo/47m08](https://www.dropbox.com/scl/fo/47m08) tp7g3 dlz6 irfn2) v/AHHv5b_VGf8 AT84 JYR0 uWOU?dl++++0&rIkey=znvudy6p54 hp4 zce11r572r&ee-1 accessed on 20 November 2024): Based on status theory, it separates edges into two categories, receiving and sending, to aggregate information, which trains model parameters using a binary cross-entropy loss function. This algorithm [21] is similar to the one suggested in this paper, but S-GNN only considers edge direction and sign with status theory without introducing reciprocity features. * BESIDE (available online: [[https://github.com/yqc01/BESIDE](https://github.com/yqc01/BESIDE)]([https://github.com/yqc01/BESIDE](https://github.com/yqc01/BESIDE)), accessed on 20 November 2024): It [22] incorporates balance and status theories to model both triangles and \"bridge\" edges in a complementary manner. Since the characteristics of \"bridging\" nodes are uncovered, the algorithm achieves good results.