arXiv:2602. 09258v2 Announce Type: replace Abstract: Deployed graph neural networks (GNNs) are frozen at deployment yet must fit clean data, generalize under distribution shifts, and remain stable to perturbations.
By Xiaoguang Guo, Zehong Wang, Jiazheng Li, Shawn Spitzel, Qi Yang, Kaize Ding, Jundong Li, Chuxu Zhang
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...
By Dooho Lee, Jinmo Lee, Minho Jeong, Kijung Shin, Jaemin Yoo
SiST‑GNN introduces a simultaneous spatial‑temporal message‑passing framework for dynamic graph neural networks, fusing per‑node temporal embeddings with spatial aggregation in a single operation. By maintaining a recurrent hidden state per node and treating it as a cross‑time edge, the model jointly reasons over topology and evolution. Experiments on link‑prediction and node‑classification benchmarks show significant improvements over prior methods, achieving up to 158% gains in live‑update link prediction and outperforming discrete‑time baselines by 7–23% in dynamic node classification.
By Shubhajit Roy, Anirban Dasgupta
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
arXiv:2603. 06952v2 Announce Type: replace Abstract: As graphs scale to billions of nodes and edges, graph Machine Learning workloads are constrained by the cost of multi-hop traversals over exponentially growing neighborhoods.
By Yuhang Song, Naima Abrar Shami, Romaric Duvignau, Vasiliki Kalavri
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.
By Zhicong Cai, Yinglong Zhang, Xiaoying Hong, Xuewen Xia, Xing Xu