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.
Advances in Neural Information Processing Systems , year=
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GPR-GAE is a novel self-supervised graph auto-encoder using multiple Generalized PageRank filters that serves as a plug-and-play purifier achieving state-of-the-art robustness for GNNs against structural attacks.
citing papers explorer
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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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Self-supervised Adversarial Purification for Graph Neural Networks
GPR-GAE is a novel self-supervised graph auto-encoder using multiple Generalized PageRank filters that serves as a plug-and-play purifier achieving state-of-the-art robustness for GNNs against structural attacks.