arXiv:2608. 02128v1 Announce Type: new Abstract: Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers.
By Antonin Joly, Nicolas Keriven, Aline Roumy
Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers. We show that several existing scalable approaches can be written as structured modifications of the GNN propagation matrix, providing a unified perspective that exposes their respective limitations.
arXiv:2502. 17614v3 Announce Type: replace Abstract: The rapid growth of graph data creates significant scalability challenges as most graph algorithms scale quadratically with size.
By Shengbo Gong, Mohammad Hashemi, Juntong Ni, Carl Yang, Wei Jin
arXiv:2606. 03495v1 Announce Type: new Abstract: Heterogeneous graph neural networks (HGNNs) have demonstrated remarkable performance in modeling complex relational data, however their interpretability in high-stakes applications remains a critical challenge.
By Zongrui Li, Yuhang Zhao, Ying Zhao, Yuanzhao Guo, Qiang Huang, Yuan Tian
arXiv:2607. 24338v1 Announce Type: new Abstract: Unsupervised graph representation learning aims to derive meaningful node embeddings by capturing both structural and attribute information without relying on labeled data.
By Zengyi Wo, Shiyu Zhang, Qiyao Peng, Tianpeng Li, Xuan Guo
arXiv:2509. 24256v2 Announce Type: replace-cross Abstract: The pretrain-transfer paradigm, which underpins the success of large language models (LLMs), has demonstrated the immense power of creating foundation models that learn generalizable representations from vast datasets.
By Yunhao Liang, Pujun Zhang, Yuan Qu, Jingyuan Yang, Shaochong Lin, Zuo-jun Max Shen