# A Classification Method for Academic Resources Based on a Graph Attention Network
## 4 Proposed Method
### Aggregation Based on an Attention Mechanism  
In the application of academic resources, existing graph attention networks only calculate the attention based on the semantic similarity of nodes, but often neglect association information between academic resource nodes. The ACGAT introduces the semantic relevance and academic relevance of academic resources into the model and integrates them. In the process of the dissemination of information of graph nodes, it is necessary to learn three distribution parameters, which are the semantic similarity, association commonality, and influence information. According to these three parameters, we get the final updated feature of the aggregated attention coefficient and classify the academic resource nodes to improve the accuracy. The ACGAT takes the semantic feature \(h=\{h_{1},h_{2},\ldots,h_{N}\},\quad h_{i}\in\mathbb{R}^{F}\) of the academic resource node as input, and all of the node features will be transformed by the linear change matrix \(Wg\). A shared attention mechanism \(\theta\) is used on the node to calculate the measurement of similarity between nodes according to the features of the input nodes. Similarly to the graph attention model [20], this model calculates the measurement of similarity between the adjacent node and the center node using Equation (5):  
\[\varepsilon_{ij}=\theta\big{(}\big{[}Wh_{i}||Wh_{j}\big{]}\big{)},j\in\mathcal{ N}_{i}, \tag{5}\]  
where \(\mathcal{N}_{i}\) is the neighbor collection of node \(i\). First, a linear-mapping sharing parameter \(W\) is used to increase the dimensions of the vertex features. Then, \([\cdot\ ||\cdot\ ]\) splices the transformed features of vertices \(i\) and \(j\). Finally, \(\theta(\cdot\ )\) maps the spliced high-dimensional features to a real number. The similarity measurement of the content features is normalized by the \(softmax\) function [27] of Equation (6) to obtain the attention coefficient \(\alpha_{ij}\) of the academic semantic features. \[\alpha_{ij}=softmax\big{(}\varepsilon_{ij}\big{)}=\frac{\exp\big{(} \varepsilon_{ij}\big{)}}{\sum_{k\in\mathcal{N}_{i}}\exp(\varepsilon_{ik})} \tag{6}\]  
The advantage of using \(LeakyReLU\) is that in the process of back propagation, the gradient can also be calculated for parts of the input of \(LeakyReLU\) activation function that  
Figure 5: Adjacency feature graph. (**a**) Original binary adjacency graph; (**b**) an asymmetric bidirectional edge feature graph of the central node. are less than zero [28]. The factor calculated by the attention mechanism is obtained using Equation (7). \[\alpha_{ij}=softmax\big{(}e_{ij}\big{)}=\frac{\exp\bigl{(}LeakyReLU\big{(}\theta \left[Wh_{i}\middle\|Wh_{j}\right]\bigr{)}\bigr{)}}{\sum_{k\in\mathcal{N}_{i}} \exp\bigl{(}LeakyReLU(\theta\left[Wh_{i}\middle\|Wh_{k}\right])\bigr{)}} \tag{7}\]  
Then, the aggregation coefficient is obtained as shown in Equation (8):  
\[\lambda_{ij}=W_{e}\alpha_{ij}+W_{e}e_{ij}+W_{D}\beta_{ij}, \tag{8}\]  
where \(W_{e}\), \(W_{e}\), and \(W_{D}\) are learning parameters that reflect the influence of three dimensions of academic semantic similarity, including academic semantic relevance, academic association commonality, and the influence coefficient. \(\lambda_{ij}\) is the coefficient of the final fusion of academic resources. The final aggregate attention coefficient is used to calculate the linear combinations of related features. The result is the final output of each node, and \(\sigma(\cdot\,)\) is the activation function, as shown in Equation (9). \[h_{i}^{\prime}=\sigma\left(\sum_{j\in\mathcal{N}_{i}}\lambda_{ij}Wh_{j}\right) \tag{9}\]  
According to the existing multiple-attention mechanism [29], \(K\) independent attention mechanisms are used to execute Equation (9), and their features are related. The essence of multiple heads is the calculation of multiple independent attentions, which acts as an integrated function to prevent over-fitting. In the last layer of the neural network, the output feature expression of the result is updated, resulting in Equation (10).