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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.IR2025★ 1 cited
Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey
Zulun Zhu, Tiancheng Huang, Kai Wang +3
Large language models (LLMs) struggle with the factual error during inference due to the lack of sufficient training data and the most updated knowledge, leading to the hallucinati…