# A Classification Method for Academic Resources Based on a Graph Attention Network
## 2 Related Work  
Graph data contain two types of information: attribute information [6] and structure information [7]. Attribute information describes the inherent properties of objects in a graph, and structure information describes the information on associations between objects. The structure generated by associations is not only helpful for the description of nodes in graph data, but also plays a key role in the description of the whole graph. It is a key challenge in graph learning to effectively learn the complex non-Euclidean structure of graph data. The existing graph-embedding methods aim to learn the low-dimensional potential representations of nodes in a network [8]. The learned feature representation can be used as a feature in various graph-based tasks, such as classification, clustering, and link prediction. The traditional methods of realizing graph learning are mainly divided into two categories. One is comprised of embedding methods based on matrix decomposition, such as the graph factorization (GF) algorithm [9] and GraRep [10]. The other is based on a random walk, such as DeepWalk [11], LINE [12], and node2 vec [13]. However, the embedding of traditional graph learning cannot capture complex patterns and does not incorporate node features, resulting in low-accuracy node classification results.  
A graph neural network (GNN) [14] is a kind of neural network model that can operate on graph structure data to convey graph information. It uses the node information and structure information of a graph to effectively mine the information contained in the graph data. GNNs have achieved excellent results in many application fields, such as image recognition [15] and heterogeneous graph learning [16]. Convolution operations have been extended to graph learning with graph convolution networks (GCNs) [17]. [PERSON] et al. [18] proposed a convolution method that was applied to a graph data model, which needed to sort graph nodes and had high complexity. [PERSON] et al. [19] mapped graph features in the time domain to the spectral frequency domain and approximately simplified them with Chebyshev polynomials, which achieved successful results in semi-supervised classification of graph nodes. In addition to graph convolution networks, many researchers have introduced attention mechanisms to implement graph node classification. [PERSON] [20] first proposed a graph attention network (GAT) that assigned different weights to different nodes in the neighborhood. [PERSON] and [PERSON] [5] added edge feature vectors directly, which extended the attention mechanism proposed for the first time and focused on each type of feature neighborhood. [PERSON] et al. [21] introduced an edge network in which the eigenvectors of edges were used as input and output matrices to transform the embedding of adjacent nodes. [PERSON] et al. [22] proposed a channel graph attention network based on edge content, which could find the fine-grained signals of node interaction from text information and improve the accuracy of node characterization. The classification results of graph nodes show that the adaptive ability of GATs makes them more effective in fusing the information of node features and graph topology.  
In existing research, it was effective to combine edge content in information aggregation. However, the feature content on the edge of the connections, such as intensity, has not been fully explored. In the classification of academic resources, ignoring the rich information on associations between resource nodes will lead to unsatisfactory classification results. Therefore, in this paper, we use semantic relevance and academic relevance (commonality between resources and influence information) for information aggregation to effectively improve the classification accuracy of academic resource network nodes.