Dual-branch subgraph-pattern attention graph neural network for link prediction

Graph data is a natural form of unstructured data, with nodes and edges representing entities and the relationships between them respectively. This type of data can be directly applied to various real-world scenarios. Graph Neural Networks (GNNs) have a natural advantage in handling graph-structured data, effectively processing the hierarchical structure of graphs to extract rich information. However, current GNNs have limitations in dealing with local information. In this paper, we discover a new attention-based network module that outperforms general GNNs. To better learn global-local information and overall hierarchical information, we propose a Dual-Branch Subgraph Pattern Attention Graph Neural Network (DB-SP-GAT). Experimental results demonstrate that DB-SP-GAT achieves superior link prediction performance across five benchmark datasets.