Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks
This addresses challenges in processing heterogeneous information networks for applications like Internet data analysis, but it appears incremental as it builds on existing GNN methods with a self-supervised framework.
This paper tackles the problem of graph neural networks (GNNs) struggling with heterogeneity and redundancy in complex graph data by proposing a self-supervised GNN model that combines additional attribute information to better mine deep features, aiming to improve adaptability and performance.
This paper explores the applications and challenges of graph neural networks (GNNs) in processing complex graph data brought about by the rapid development of the Internet. Given the heterogeneity and redundancy problems that graph data often have, traditional GNN methods may be overly dependent on the initial structure and attribute information of the graph, which limits their ability to accurately simulate more complex relationships and patterns in the graph. Therefore, this study proposes a graph neural network model under a self-supervised learning framework, which can flexibly combine different types of additional information of the attribute graph and its nodes, so as to better mine the deep features in the graph data. By introducing a self-supervisory mechanism, it is expected to improve the adaptability of existing models to the diversity and complexity of graph data and improve the overall performance of the model.