Research on Graph Data Classification Algorithm Based on Deep Learning Technology

With the rapid development of science and technology, the widespread application of graph data in various fields has increasingly highlighted its importance. Social networks, bioinformatics, knowledge graphs and other fields are all inseparable from the processing and analysis of graph data. The classification task of graph data is a key part of the in-depth exploration and understanding of these complex relationships. In this context, the rise of deep learning technology has brought new ideas and methods to graph data classification. Deep learning technology, with its powerful feature extraction and pattern recognition capabilities, has demonstrated excellent performance in fields such as images, speech, and natural language. Based on this cutting-edge technology, this paper conducts an in-depth study of graph data classification algorithms based on deep learning technology, focusing on two key aspects: attention network and cost sensitivity. In terms of attention network, we adopt the Graph Attention Network (GAT) as a feature extraction tool for graph data. GAT simulates the human attention mechanism and can dynamically allocate weights to highlight important information while suppressing irrelevant information, thereby better capturing the structure and attribute information of graph data. On the other hand, cost-sensitive techniques are learning methods that take into account the different costs of different classification errors. In this paper, we adopt cost-sensitive graph neural network (CS-GNN) as a classification tool for graph data. CS-GNN can dynamically adjust the loss function and gradient descent direction according to different categories and error costs to improve classification efficiency and accuracy. To sum up, this article's research on graph data classification algorithm based on deep learning technology provides important ideas and methods for the analysis and mining of graph data. These results are not only expected to promote the development of graph data classification algorithms in the academic field, but will also play an important role in practical applications and promote scientific and technological progress and innovative development in various fields.