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Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data by generating novel graph structures.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
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Dropedge: Towards deep graph convolutional networks on node classification
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2019 · 1907
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Learning discrete structures for graph neural networks
Luca Franceschi, Mathias Niepert, Massimiliano Pontil, and Xiao He. 2019 · 1982
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Graph-based dependency parsing with graph neural networks
Tao Ji, Yuanbin Wu, and Man Lan. 2019 · 2019
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Iterative deep graph learning for graph neural networks: Better and robust node embeddings
Yu Chen, Lingfei Wu, and Mohammed Zaki. 2020 · 2020
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Generalization and representational limits of graph neural networks
Vikas Garg, Stefanie Jegelka, and Tommi Jaakkola. 2020 · 2020
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Graph structure learning for robust graph neural networks
Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, and Jiliang Tang. 2020 · 2020
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Ernie 2.0: A continual pre-training framework for language understanding
Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Hao Tian, Hua Wu, and Haifeng Wang. 2020 · 2020
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Graph-revised convolutional network
Donghan Yu, Ruohong Zhang, Zhengbao Jiang, Yuexin Wu, and Yiming Yang. 2020 · 2020
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Robust graph representation learning via neural sparsification
Cheng Zheng, Bo Zong, Wei Cheng, Dongjin Song, Jingchao Ni, Wenchao Yu, Haifeng Chen, and Wei Wang. 2020 · 2020
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Glm: General language model pretraining with autoregressive blank infilling
Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, and Jie Tang. 2021 · 2021
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Slaps: Self-supervision improves structure learning for graph neural networks
Bahare Fatemi, Layla El Asri, and Seyed Mehran Kazemi. 2021 · 2021
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Task-adaptive neural process for user cold-start recommendation
Xixun Lin, Jia Wu, Chuan Zhou, Shirui Pan, Yanan Cao, and Bin Wang. 2021 · 2021
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Is homophily a necessity for graph neural networks?
Yao Ma, Xiaorui Liu, Neil Shah, and Jiliang Tang. 2021 · 2021
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Masked label prediction: Unified message passing model for semi-supervised classification
Yunsheng Shi, Zhengjie Huang, Shikun Feng, Hui Zhong, Wenjin Wang, and Yu Sun. 2021 · 2021
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Graph neural networks with adaptive readouts
David Buterez, Jon Paul Janet, Steven J Kiddle, Dino Oglic, and Pietro Liò. 2022 · 2022
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Towards robust graph neural networks for noisy graphs with sparse labels
Enyan Dai, Wei Jin, Hui Liu, and Suhang Wang. 2022 · 2022
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Variational inference for training graph neural networks in low-data regime through joint structure-label estimation
Neighborhood homophily-based graph convolutional network
Shengbo Gong, Jiajun Zhou, Chenxuan Xie, and Qi Xuan. 2023 · 2023
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Explanations as features: Llm-based features for text-attributed graphs
Xiaoxin He, Xavier Bresson, Thomas Laurent, and Bryan Hooi. 2023 · 2023
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Restructuring graph for higher homophily via adaptive spectral clustering
Shouheng Li, Dongwoo Kim, and Qing Wang. 2023 · 2023
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Representation learning with large language models for recommendation
Xubin Ren, Wei Wei, Lianghao Xia, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang. 2023 · 2023
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Graphgpt: Graph instruction tuning for large language models
Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Lixin Su, Suqi Cheng, Dawei Yin, and Chao Huang. 2023 · 2023
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Reliable representations make a stronger defender: Unsupervised structure refinement for robust gnn
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Nodeformer: A scalable graph structure learning transformer for node classification
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Aquilachat-7b
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Llama 2: Open foundation and fine-tuned chat models
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Llmrec: Large language models with graph augmentation for recommendation
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Augmenting low-resource text classification with graph-grounded pre-training and prompting
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Empower text-attributed graphs learning with large language models (llms)
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Self-supervised graph structure refinement for graph neural networks
Jianan Zhao, Qianlong Wen, Mingxuan Ju, Chuxu Zhang, and Yanfang Ye. 2023 · 2023
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Opengsl: A comprehensive benchmark for graph structure learning
Zhiyao Zhou, Sheng Zhou, Bochao Mao, Xuanyi Zhou, Jiawei Chen, Qiaoyu Tan, Daochen Zha, Can Wang, Yan Feng, and Chun Chen. 2023 · 2023
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Se-gsl: A general and effective graph structure learning framework through structural entropy optimization
Dongcheng Zou, Hao Peng, Xiang Huang, Renyu Yang, Jianxin Li, Jia Wu, Chunyang Liu, and Philip S Yu. 2023 · 2023
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