# A Classification Method for Academic Resources Based on a Graph Attention Network
## 5 Experiment
### Experimental Analysis  
We dealt with the three datasets individually. When the authors in the scholar cooperation network of the the SIG had the same name, we adopted the method of \"author name + organization\" to determine unique authors. The cooperation intensity between authors was the feature of the association commonality between nodes. In the citation network datasets, Cora and Citeseer, we calculated the shortest path between nodes as the feature of the association commonality between nodes and constructed an incidence matrix of the citation networks for node classification. The three networks constructed with this method are shown in Figure 6, in which the isolated nodes in the datasets are removed from the Citeseer network diagram. Different sizes of nodes in the network graph indicate the influence of academic resources on the network graph. Large nodes have great influence on adjacent nodes and obvious aggregation effects. We compared the proposed method with five existing benchmark methods--DeepWalk [11], LINE (2 nd) [12], structural deep network embedding (SDNE) [31], a graph convolutional network (GCN) [19], and a graph attention network (GAT) [20]. We also compared it with versions of the proposed model that only add the association commonality feature (A-GAT) or only add the association influence factor (C-GAT). Due to space limitations, we show only visualized classification results of the traditional methods (Figures 7-9). We used classification accuracy as the experimental index, which was obtained from Equation (11) [32]. \[\text{Accuracy }=\frac{TP+TN}{TP+FP+TN+FN}, \tag{11}\]  
where TP (true positives) is the number of positive cases that are correctly classified. FP (false positives) is the number of false positive cases. FN (false negatives) is the number of false negatives. TN (true negatives) is the number of cases that are correctly classified as negative. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & & **Nodes** & **Edges** & **Features** & **Classes** \\ \hline Scholar cooperation network & SIG & 3669 & 10,399 & 3664 & 8 \\ \hline \multirow{2}{*}{Citation network} & Cora & 2708 & 5429 & 1433 & 7 \\ \cline{2-6}  & Citeseer & 3312 & 4732 & 3708 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the datasets. Figure 6: The networks constructed for the three datasets. (**a**) SIG scholar collaboration network. (**b**) Cora citation network. (**c**) Citeseer citation network. In the networks, different colors represent different categories, and the size of a node represents its influence on other nodes. Figure 7: A comparison of the visualization results of the three methods for the SIG dataset. (**a**) The result of DeepWalk, (**b**) the result of LINE, and (**c**) the result of structural deep network embedding (SDNE). Differently colored nodes represent different categories. The dataset contains eight categories (Ada programming language (DA); computer graphics (GRAPH); information retrieval (IR); information technology education (ITE); accessibility and computing (ACCESS); artificial intelligence (AI); computational biology (BIO); knowledge discovery in data (KDD)). The classification results for scholars in the SIG are shown in Figure 10. The classification results for papers in the Cora and Citeseer citation networks are shown in Figure 11.  
In the experiment on the scholar network dataset SIG, the use of A-GAT means that only the cooperation intensity relationships between scholars are added as the association commonality feature for the model; the use of C-GAT means that only the weights of the scholar nodes were added as the content influence feature for the model. As shown in  
Figure 8: A comparison of the visualization results of the three methods for the Cora dataset. (**a**) The result of DeepWalk, (**b**) the result of LINE, and (**c**) the result of SDNE. Differently colored nodes represent different categories.