自监督GNN提升异构图特征提取，更灵活挖掘深层关系。
Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks
- 设计自监督框架，动态融合节点属性与图结构信息
- 在多个异构数据集上显著提升特征表示能力
- 适合处理复杂、冗余的异构图数据，提升模型泛化性
本文研究了图神经网络（GNN）在互联网快速发展背景下处理复杂图数据的应用与挑战。由于图数据常存在异构性和冗余性，传统GNN方法过度依赖图的初始结构和属性信息，限制了其对图中复杂关系与模式的准确建模能力。为此，本文提出一种基于自监督学习框架的图神经网络模型，能够灵活融合属性图及其节点的多种附加信息，以更好地挖掘图数据中的深层特征。通过引入自监督机制，期望提升现有模型对图数据多样性和复杂性的适应能力，并改善整体性能。
原文摘要 · Abstract (English)
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.
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