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cs.SI2025
Masked Language Models are Good Heterogeneous Graph Generalizers
Jinyu Yang, Cheng Yang, Shanyuan Cui +5
Heterogeneous graph neural networks (HGNNs) excel at capturing structural and semantic information in heterogeneous graphs (HGs), while struggling to generalize across domains and…
cs.IR2025
CORONA: A Coarse-to-Fine Framework for Graph-based Recommendation with Large Language Models
Junze Chen, Xinjie Yang, Cheng Yang +4
Recommender systems (RSs) are designed to retrieve candidate items a user might be interested in from a large pool. A common approach is using graph neural networks (GNNs) to captu…
cs.CL2025
GraphLAMA: Enabling Efficient Adaptation of Graph Language Models with Limited Annotations
Junze Chen, Cheng Yang, Shujie Li +4
Large language models (LLMs) have demonstrated their strong capabilities in various domains, and have been recently integrated for graph analysis as graph language models (GLMs). W…