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The integration of Large Language Models (LLMs) with Graph Representation Learning (GRL) marks a significant evolution in analyzing complex data structures.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
Earlier work this paper cites.
Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
A comprehensive survey of graph embedding: Problems, techniques, and applications
Hongyun Cai, Vincent W Zheng, and Kevin Chen-Chuan Chang · 2018
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
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Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le · 2019
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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
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Graph-based modeling of online communities for fake news detection
Shantanu Chandra, Pushkar Mishra, Helen Yannakoudakis, Madhav Nimishakavi, Marzieh Saeidi, and Ekaterina Shutova · 2020
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Gpt-gnn: Generative pre-training of graph neural networks
Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, and Yizhou Sun · 2020
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Self-supervised graph transformer on large-scale molecular data
Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang · 2020
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
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Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen · 2020
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Text2mol: Cross-modal molecule retrieval with natural language queries
Carl Edwards, ChengXiang Zhai, and Heng Ji · 2021
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen · 2021
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Multi-modal molecule structure-text model for text-based retrieval and editing
Shengchao Liu, Weili Nie, Chengpeng Wang, Jiarui Lu, Zhuoran Qiao, Ling Liu, Jian Tang, Chaowei Xiao, and Anima Anandkumar · 2022
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Biogpt: generative pre-trained transformer for biomedical text generation and mining
Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon, and Tie-Yan Liu · 2022
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Transformer for graphs: An overview from architecture perspective
Erxue Min, Runfa Chen, Yatao Bian, Tingyang Xu, Kangfei Zhao, Wenbing Huang, Peilin Zhao, Junzhou Huang, Sophia Ananiadou, and Yu Rong · 2022
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A molecular multimodal foundation model associating molecule graphs with natural language
Bing Su, Dazhao Du, Zhao Yang, Yujie Zhou, Jiangmeng Li, Anyi Rao, Hao Sun, Zhiwu Lu, and Ji-Rong Wen · 2022
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Gppt: Graph pre-training and prompt tuning to generalize graph neural networks
Mingchen Sun, Kaixiong Zhou, Xin He, Ying Wang, and Xin Wang · 2022
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Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
Elad Ben Zaken, Yoav Goldberg, and Shauli Ravfogel · 2022
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Learning on large-scale text-attributed graphs via variational inference
One for all: Towards training one graph model for all classification tasks
Hao Liu, Jiarui Feng, Lecheng Kong, Ningyue Liang, Dacheng Tao, Yixin Chen, and Muhan Zhang · 2023
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Towards graph foundation models: A survey and beyond
Jiawei Liu, Cheng Yang, Zhiyuan Lu, Junze Chen, Yibo Li, Mengmei Zhang, Ting Bai, Yuan Fang, Lichao Sun, Philip S Yu, et al · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2023
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Graphprompt: Unifying pre-training and downstream tasks for graph neural networks
Zemin Liu, Xingtong Yu, Yuan Fang, and Xinming Zhang · 2023
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Large language models generate functional protein sequences across diverse families
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Jianan Zhao, Meng Qu, Chaozhuo Li, Hao Yan, Qian Liu, Rui Li, Xing Xie, and Jian Tang · 2022
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Congrat: Self-supervised contrastive pretraining for joint graph and text embeddings
William Brannon, Suyash Fulay, Hang Jiang, Wonjune Kang, Brandon Roy, Jad Kabbara, and Deb Roy · 2023
Cited alongside, same era.
Exploring the potential of large language models (llms) in learning on graphs
Zhikai Chen, Haitao Mao, Hang Li, Wei Jin, Hongzhi Wen, Xiaochi Wei, Shuaiqiang Wang, Dawei Yin, Wenqi Fan, Hui Liu, et al · 2023
Cited alongside, same era.
Label-free node classification on graphs with large language models (llms)
Zhikai Chen, Haitao Mao, Hongzhi Wen, Haoyu Han, Wei Jin, Haiyang Zhang, Hui Liu, and Jiliang Tang · 2023
Cited alongside, same era.
Simteg: A frustratingly simple approach improves textual graph learning
Keyu Duan, Qian Liu, Tat-Seng Chua, Shuicheng Yan, Wei Tsang Ooi, Qizhe Xie, and Junxian He · 2023
Cited alongside, same era.
Jiayan Guo, Lun Du, and Hengyu Liu · 2023
Cited alongside, same era.
Explanations as features: Llm-based features for text-attributed graphs
Xiaoxin He, Xavier Bresson, Thomas Laurent, and Bryan Hooi · 2023
Cited alongside, same era.
Beyond text: A deep dive into large language models’ ability on understanding graph data
Yuntong Hu, Zheng Zhang, and Liang Zhao · 2023
Cited alongside, same era.
Ali Madani, Ben Krause, Eric R Greene, Subu Subramanian, Benjamin P Mohr, James M Holton, Jose Luis Olmos Jr, Caiming Xiong, Zachary Z Sun, Richard Socher, et al · 2023
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Train your own gnn teacher: Graph-aware distillation on textual graphs
Costas Mavromatis, Vassilis N Ioannidis, Shen Wang, Da Zheng, Soji Adeshina, Jun Ma, Han Zhao, Christos Faloutsos, and George Karypis · 2023
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Large language models as topological structure enhancers for text-attributed graphs
Shengyin Sun, Yuxiang Ren, Chen Ma, and Xuecang Zhang · 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
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Can language models solve graph problems in natural language?
Heng Wang, Shangbin Feng, Tianxing He, Zhaoxuan Tan, Xiaochuang Han, and Yulia Tsvetkov · 2023
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Prompt tuning on graph-augmented low-resource text classification
Zhihao Wen and Yuan Fang · 2023
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Graph-aware language model pre-training on a large graph corpus can help multiple graph applications
Han Xie, Da Zheng, Jun Ma, Houyu Zhang, Vassilis N Ioannidis, Xiang Song, Qing Ping, Sheng Wang, Carl Yang, Yi Xu, et al · 2023
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Natural language is all a graph needs
Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu, and Yongfeng Zhang · 2023
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Empower text-attributed graphs learning with large language models (llms)
Jianxiang Yu, Yuxiang Ren, Chenghua Gong, Jiaqi Tan, Xiang Li, and Xuecang Zhang · 2023
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Large graph models: A perspective
Ziwei Zhang, Haoyang Li, Zeyang Zhang, Yijian Qin, Xin Wang, and Wenwu Zhu · 2023
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Graphtext: Graph reasoning in text space
Jianan Zhao, Le Zhuo, Yikang Shen, Meng Qu, Kai Liu, Michael Bronstein, Zhaocheng Zhu, and Jian Tang · 2023
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A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny · 2023
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Pretraining language models with text-attributed heterogeneous graphs
Tao Zou, Le Yu, Yifei Huang, Leilei Sun, and Bowen Du · 2023
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