Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors

• URL: http://arxiv.org/abs/2604.19028v1
• Date: Tue, 21 Apr 2026 03:23:34 GMT
• Title: Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors
• Abstract summary: Graph neural networks (GNNs) face a fundamental limitation: they require separate training for each new graph.<n>We introduce NodePFN, which generalizes to arbitrary graphs without graph-specific training.<n>A single pre-trained NodePFN achieves 71.27 average accuracy on 23 benchmarks.
• Score: 32.18961351570798
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the most challenging problems in graph machine learning is generalizing across graphs with diverse properties. Graph neural networks (GNNs) face a fundamental limitation: they require separate training for each new graph, preventing universal generalization across diverse graph datasets. A critical challenge facing GNNs lies in their reliance on labeled training data for each individual graph, a requirement that hinders the capacity for universal node classification due to the heterogeneity inherent in graphs -- differences in homophily levels, community structures, and feature distributions across datasets. Inspired by the success of large language models (LLMs) that achieve in-context learning through massive-scale pre-training on diverse datasets, we introduce NodePFN. This universal node classification method generalizes to arbitrary graphs without graph-specific training. NodePFN learns posterior predictive distributions (PPDs) by training only on thousands of synthetic graphs generated from carefully designed priors. Our synthetic graph generation covers real-world graphs through the use of random networks with controllable homophily levels and structural causal models for complex feature-label relationships. We develop a dual-branch architecture combining context-query attention mechanisms with local message passing to enable graph-aware in-context learning. Extensive evaluation on 23 benchmarks demonstrates that a single pre-trained NodePFN achieves 71.27 average accuracy. These results validate that universal graph learning patterns can be effectively learned from synthetic priors, establishing a new paradigm for generalization in node classification.

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