Zhe Xu, Kaveh Hassani, Si Zhang, Hanqing Zeng, Michihiro Yasunaga, Limei Wang, Dongqi Fu, Ning Yao, Bo Long, Hanghang Tong
| Challenge: | Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs). |
| Approach: | They propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the art (SOTA) GNNs on node classification tasks without requiring any architectural modifications. |
| Outcome: | The proposed approach outperforms existing GNNs on node classification tasks and is open-source upon publication. |
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Demystifying the Power of Large Language Models in Graph Generation (2025.findings-naacl)
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| Challenge: | True. True. False |
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Methods for Estimating and Improving Robustness of Language Models (2022.naacl-srw)
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| Challenge: | Graph neural networks (GNNs) are used to learn document representation from graph structures. |
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