arXiv Machine Learning
DualGSL: Prior-Guided Graph Structure Learning with Node-Adaptive Prediction Fusion
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arXiv Machine Learning
Oct 7
Complementary Supervised and Self-Supervised Representations for Out-of-Distribution Graph Learning
The paper investigates how self‑supervised graph representations can complement supervised ones for out‑of‑distribution (OOD) node classification. Two backbone‑agnostic frameworks are introduced: Co‑Train, which jointly learns and integrates supervised and SSL representations during training, and Dual‑Space Retrieval, which performs non‑parametric prediction in both representation spaces and fuses the results at inference. Experiments on four graph benchmarks with various GNN backbones and SSL objectives (DGI and GRACE) show that Co‑Train consistently outperforms strong supervised OOD baselines, while Dual‑Space Retrieval offers competitive, flexible performance, demonstrating that SSL provides complementary signals that improve OOD node classification.
Hugging Face Trending Papers
Oct 6
Complementary Supervised and Self-Supervised Representations for Out-of-Distribution Graph Learning
Out-of-distribution (OOD) generalization remains challenging for graph neural networks (GNNs), as graph distributions can vary substantially across time and domains. Supervised and self-supervised gra...
arXiv Machine Learning
Sep 23
PreGS: A Parameter-Transfer-Based Multi-Expert Graph Neural Network for Node Classification
PreGS is a multi-expert graph neural network that uses parameter transfer from a pre‑trained multi‑head GAT to freeze GraphSAGE experts, creating complementary structural branches. The model fuses raw node features, GAT head outputs, and expert representations through an MLP, then combines the result with pretrained GAT logits. An extended version, PreGSv2, adds source‑level weighting and a structural gating mechanism for adaptive feature integration, and both variants outperform several baseline GNNs on eight public datasets.
arXiv AI
Aug 6
NodeJEPA: Structure-Conditioned Latent Prediction for Node-Level Graph Self-Supervised Learning
arXiv:2608. 04381v1 Announce Type: cross Abstract: Self-supervised learning on graphs is largely shaped by contrastive methods that depend on carefully designed augmentations, and by generative methods that reconstruct node attributes in the input space.
arXiv Machine Learning
Aug 3
Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models
arXiv:2607. 28980v1 Announce Type: new Abstract: Graph Foundation Models (GFMs) have recently emerged as a promising paradigm for enabling knowledge transfer across diverse domains.
arXiv Machine Learning
Sep 30
Message Passing Does More with Less for In-Context Learning on Graphs
arXiv:2609.37057v1 Announce Type: new Abstract: Achieving strong performance with graph neural networks (GNNs) typically requires training and hyperparameter tuning for each dataset, incurring repeat...