arXiv:2610.02517v1 Announce Type: new
Abstract: Graph-structured data plays a pivotal role in modeling complex relationships. However, real-world graphs are often incomplete due to data collection an...
By Yu Song, Zhigang Hua, Yan Xie, Bingheng Li, Jingzhe Liu, Bo Long, Jiliang Tang, Hui Liu
arXiv:2607. 17272v1 Announce Type: new Abstract: Node representation learning has advanced rapidly, yet most existing methods rely on per-dataset training and hyperparameter tuning.
By Dooho Lee, Jaemin Yoo
GraphIFE addresses the class imbalance problem in graph-structured data by tackling a quality inconsistency issue in synthesized nodes. The framework uses graph invariant learning to strengthen embedding space representations and identify invariant features, leading to improved performance on minority classes. Experiments show that GraphIFE consistently outperforms various baselines across multiple datasets.
By Fanlong Zeng, Wensheng Gan, Kangjie Chen, Philip S. Yu
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...
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.
By Qingying Hao, Zikang Chen, Chuxuan Hu, Jinyuan Jia, Bo Li, Gang Wang, Carl Gunter
Graph-level out-of-distribution (OOD) generalization is critical for deploying graph neural networks in real-world applications where distribution shifts are inevitable. Existing approaches typically...