Graph Generalization

Graph generalization focuses on developing machine learning models that accurately predict graph properties across diverse datasets and unseen distributions, overcoming limitations of traditional methods that assume identical data distributions during training and testing. Current research emphasizes improving out-of-distribution generalization using causal inference, invariant feature learning (e.g., via information bottleneck methods), and novel architectures like Graph Neural Networks (GNNs) and transformers adapted for graph data. These advancements are crucial for enhancing the reliability and applicability of graph-based machine learning in various domains, including network analysis and complex system modeling, where data distributions often vary significantly.