The content delves into the pivotal role of graph learning in various applications and the challenges posed by distribution shifts. It categorizes methods into domain adaptation, out-of-distribution learning, and continual learning scenarios. Various models and frameworks are discussed to enhance generalization capabilities and adaptability to new data distributions.
Graph learning is crucial in diverse domains like social networks, biological networks, and recommender systems.
Real-world graph data dynamics pose challenges due to distribution shifts affecting model performance.
Methods like DA-AGE, UDA-HGM, GTRANS, and Uncertainty-GNN address OOD detection and generalization issues.
Frameworks like GAPGC, GRAND, LMN focus on improving model performance through data augmentation and model development.
OOD detection models like GraphDE and GOOD-D utilize variational inference and generative models for accurate classification.
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