PD-GATv2: positive difference second generation graph attention network based on multi-granularity in information systems to classification

The Graph Attention Network (GAT) is a widely recognized architecture in the field of Graph Neural Networks (GNNs). It is considered the state-of-the-art approach for graph representation learning. In recent years, several researchers have successfully applied GAT to structured Euclidean data, including images and languages. Additionally, the Graph Convolutional Network (GCN) has also been adopted in information systems, such as PN-GCN, which establishes undirected graphs for classification. However, compared to undirected graphs, directed graphs contain directional information that more comprehensively describes the relations between objects in the graph. Therefore, we establish a directed graph in the information system and further analyze it using GAT. As a first step, this article introduces the concept of multi-granularity object directed weighted graphs. Then, we construct the second generation of the residual edge-weighted graph attention neural network model (PD-GATv2) based on these directed graphs. Finally, we verify the effectiveness and generalizability of the PD-GATv2 algorithm through experiments, and its effectiveness is further demonstrated through ablation experiments.