TIDE decomposes graph information into feature-specific, structure-specific, and joint components to retain only label-relevant joint signals and improve OOD detection over standard supervised learning.
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R-GFM constructs multi-scale Riemannian graph-of-graphs to learn geometry-adaptive representations, reducing structural domain generalization error and delivering up to 49% relative gains on downstream graph tasks.
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What Information Matters? Graph Out-of-Distribution Detection via Tri-Component Information Decomposition
TIDE decomposes graph information into feature-specific, structure-specific, and joint components to retain only label-relevant joint signals and improve OOD detection over standard supervised learning.
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Learning Graph Foundation Models on Riemannian Graph-of-Graphs
R-GFM constructs multi-scale Riemannian graph-of-graphs to learn geometry-adaptive representations, reducing structural domain generalization error and delivering up to 49% relative gains on downstream graph tasks.