arXiv AI
Decoupled Structure-Feature Alignment via Alternating Optimization for Graph Learning
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Structure-Feature Aligned Graph Learning via Alternating Constrained Optimization
arXiv:2607. 11577v1 Announce Type: cross Abstract: We introduce a constrained two-view framework for node prediction that aligns structure-conditioned GNN embeddings with a structure-free feature prior learned by an anchor model.
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Unsupervised Graph Representation Learning with Complementary View Alignment
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Enhancing Distance-Based Graph Autoencoders with Structural Penalties for Dynamic Graph Embedding
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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.