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cs.LG2026
Modality-free Graph In-context Alignment
Wei Zhuo, Siqiang Luo
In-context learning (ICL) converts static encoders into task-conditioned reasoners, enabling adaptation to new data from just a few examples without updating pretrained parameters.…
cs.LG2025
MoSE: Unveiling Structural Patterns in Graphs via Mixture of Subgraph Experts
Junda Ye, Zhongbao Zhang, Li Sun +1
While graph neural networks (GNNs) have achieved great success in learning from graph-structured data, their reliance on local, pairwise message passing restricts their ability to…
cs.LG2024★ 3 cited
Towards Graph Foundation Models: Training on Knowledge Graphs Enables Transferability to General Graphs
Kai Wang, Siqiang Luo, Caihua Shan +1
Inspired by the success of large language models, there is a trend toward developing graph foundation models to conduct diverse downstream tasks in various domains. However, curren…