Core Concepts
In this comprehensive survey, the authors explore the challenges of distribution shifts in graph learning and present various methods to address them, categorizing existing approaches into three essential scenarios. They aim to provide guidance for developing effective graph learning algorithms and stimulate future research in this area.
Abstract
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.
Stats
GAUG utilizes a differentiable edge predictor for augmented graphs.
DAGNN employs domain-adversarial training for feature optimization.
GPN combines node classification with OOD detection using variational inference.
GraphDE integrates latent variables for OOD detection and debiased learning.
Quotes
"Models trained on one dataset may not generalize well to a new dataset with different characteristics."
"Distribution shifts complicate graph learning processes."
"OOD detection is crucial for identifying unknown or significantly different patterns."