NodePFN pre-trains on synthetic graphs with controllable homophily and causal feature-label models to achieve 71.27 average accuracy on 23 node classification benchmarks without graph-specific training.
arXiv preprint arXiv:2401.04301 , year=
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Transformer layers are analogous to power method steps, tilting tokens toward the principal eigenvector of the output-value weight product, with stronger analytical and empirical alignment in shared-weight models and a proposed steering method.
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Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors
NodePFN pre-trains on synthetic graphs with controllable homophily and causal feature-label models to achieve 71.27 average accuracy on 23 node classification benchmarks without graph-specific training.
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Analogies between Transformer Layers and Power Method
Transformer layers are analogous to power method steps, tilting tokens toward the principal eigenvector of the output-value weight product, with stronger analytical and empirical alignment in shared-weight models and a proposed steering method.