A survey of deep graph learning under distribution shifts: from graph out-of-distribution generalization to adaptation

K Zhang, S Liu, S Wang, W Shi, C Chen, P Li… - arxiv preprint arxiv …, 2024 - arxiv.org
Distribution shifts on graphs--the discrepancies in data distribution between training and
employing a graph machine learning model--are ubiquitous and often unavoidable in real …

Raising the Bar in Graph OOD Generalization: Invariant Learning Beyond Explicit Environment Modeling

X Shen, Y Liu, Y Wang, R Miao, Y Dai, S Pan… - arxiv preprint arxiv …, 2025 - arxiv.org
Out-of-distribution (OOD) generalization has emerged as a critical challenge in graph
learning, as real-world graph data often exhibit diverse and shifting environments that …

BrainOOD: Out-of-distribution Generalizable Brain Network Analysis

J Xu, Y Chen, X Dong, M Lan, T Huang, Q Bian… - arxiv preprint arxiv …, 2025 - arxiv.org
In neuroscience, identifying distinct patterns linked to neurological disorders, such as
Alzheimer's and Autism, is critical for early diagnosis and effective intervention. Graph …