"图机器学习峰会-分布外鲁棒图学习新进展"

Graph machine learning has seen significant advancements in the area of out-of-distribution (OOD) graph learning, as evidenced by the content presented in the "SOME ADVANCES IN OUT-OF-DISTRIBUTION GRAPH LEARNING" paper by Yatao Bian. This paper, presented at the Graph Machine Learning Summit 1-5, discusses new developments in OOD graph learning and presents a testbed for graph OOD learning called DrugOOD. The paper highlights the importance of OOD graph learning in the context of drug discovery, which is a long and expensive process that takes more than 10 years and $1 billion to develop a new drug. This underscores the need for efficient and effective methods for graph learning, particularly in OOD scenarios. The DrugOOD testbed is specifically designed for OOD graph learning and is based on subgraph-based invariant graph learning. The key focus of the paper is on the development of methods for OOD graph learning, which is crucial for addressing the challenges of developing new drugs. The use of subgraph-based invariant graph learning is highlighted as a promising approach for OOD graph learning, and the DrugOOD testbed provides a platform for testing and evaluating the effectiveness of these methods. Overall, the paper presented by Yatao Bian at the Graph Machine Learning Summit 1-5 provides valuable insights into the latest advancements in OOD graph learning. The development of the DrugOOD testbed and the focus on subgraph-based invariant graph learning are particularly significant, as they have the potential to contribute to the advancement of graph machine learning in the context of challenging OOD scenarios. These developments have important implications for various fields, including drug discovery, and demonstrate the potential for continued progress in OOD graph learning.