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A survey of deep graph learning under distribution shifts: from graph out-of-distribution generalization to adaptation
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 …
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
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 …
learning, as real-world graph data often exhibit diverse and shifting environments that …
BrainOOD: Out-of-distribution Generalizable Brain Network Analysis
In neuroscience, identifying distinct patterns linked to neurological disorders, such as
Alzheimer's and Autism, is critical for early diagnosis and effective intervention. Graph …
Alzheimer's and Autism, is critical for early diagnosis and effective intervention. Graph …