# A Classification Method for Academic Resources Based on a Graph Attention Network
## 4 Proposed Method
### Mining and Representation of Academic Relevance  
Academic relevance includes the influence information and the commonality between resources. Inspired by PageRank [24], the influence of the node is of great significance for the aggregation of information of neighboring nodes. However, the existing model does not consider the influence factor. In collaborative networks, the author weight represents the degree of activity of authors in a certain research field. In citation networks, node weights represent the academic relevance and influence in the networks. Thus, the ACGAT introduces the influence factor of the node, thus enhancing the positive effect of the node on the network and weakening the possible negative effects of isolated nodes. The calculation method for the influence factor matrix \(A\) is as follows:  
* We calculate the degree matrix \(D\) of a network of \(N\) nodes, where \(d_{(v_{i})}\) is the degree of node \(v_{i}\). \[D=\left( \begin{array}{cccc}d_{(v_{1})}&0&\dots&0\\ 0&d_{(v_{2})}&\dots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\dots&d_{(v_{N})}\end{array} \right)\]
* The influence \(D_{ij}\) of neighboring node \(j\) on central node \(i\) is expressed as the ratio of the degree of node \(j\) to the degree of node \(i\), which can be obtained according to Equation (1). * The influence factor \(\beta_{ij}\) of each node and \(A=\left( \begin{array}{cccc}\beta_{11}&\beta_{12}&\dots&\beta_{1N}\\ \beta_{21}&\beta_{22}&\dots&\beta_{2N}\\ \vdots&\vdots&\ddots&\vdots\\ \beta_{N1}&\beta_{N2}&\dots&\beta_{NN}\end{array} \right)\) are obtained through normalization according to Equation (2). \[D_{ij}=\frac{d_{j}}{d_{i}}\] (1) \[\beta_{ij}=\frac{D_{ij}}{\sum_{m}^{M}D_{im}}\] (2)  
\(D_{ij}\) is the influence of node \(j\) on node \(i\). In the author cooperation network, \(D_{ij}\) is the ratio of the number of collaborators of author \(j\) to that of author \(i\). In the citation network, \(D_{ij}\) is the ratio of the number of citation associations of paper \(j\) to that of paper \(i\). \(\beta_{ij}\) is the influence factor of the normalized final node \(j\) on node \(i\). In this paper, we not only introduce the influence of academic resource nodes, but also integrate the association commonality between nodes into the model. In the citation network, the citation relationship between papers is a sparse matrix. We refer to the network-embedded model LINE [12], which not only obtains the local similarity of two nodes in the network, but also retains the second-order similarity between a pair of nodes \((u,v)\), that is, the similarity of their adjacent network structures. Citation networks have not only first-order similarity in content, but also second-order similarity in structure [25]. If the second-order similarity is high, there are no direct citation relationships, but there are a large number of co-cited relationships between papers. The higher the correlation, the more similar the two papers are. As shown in Figure 3, the relationship between paper 6 and paper 7 is a first-order local similarity, while the relationship between paper 5 and paper 6 belongs to second-order global similarity because they have the same co-citations, but no direct reference relationship. Based on this, when we construct the correlation matrix in the citation network, we are not limited to the information of directly related papers. We consider the papers that are adjacent and similar in structure, but have no real citation relationships. The shortest path [26] between nodes can exactly reflect the structural relationships between authors. We calculate the shortest path between nodes in the citation network as the association information between nodes. According to Equation (3), weget the coefficient \(q_{ij}\) as the coefficient of association commonality between two nodes in the citation network. \(q_{ij}\) is positively correlated with the degree of association and the commonality between two papers.