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In recent years, large language models (LLMs) have emerged as promising candidates for graph tasks.
Three scenarios for continual learning
Gido M Van de Ven and Andreas S Tolias. 2019 · 1904
Earlier work this paper cites.
Xlnet: Generalized autoregressive pretraining for language understanding
Z Yang. 2019 · 1906
Earlier work this paper cites.
Sentence-bert: Sentence embeddings using siamese bert-networks
N Reimers. 2019 · 1908
Earlier work this paper cites.
Automating the construction of internet portals with machine learning
Andrew Kachites McCallum, Kamal Nigam, Jason Rennie, and Kristie Seymore. 2000 · 2000
Earlier work this paper cites.
Graph-bert: Only attention is needed for learning graph representations
Jiawei Zhang, Haopeng Zhang, Congying Xia, and Li Sun. 2020 · 2001
Earlier work this paper cites.
Deberta: Decoding-enhanced bert with disentangled attention
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2020 · 2006
Earlier work this paper cites.
Exploring network structure, dynamics, and function using networkx
Aric Hagberg, Pieter J Swart, and Daniel A Schult. 2008 · 2008
Earlier work this paper cites.
Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008 · 2008
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Earlier work this paper cites.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017 · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin. 2018 · 2018
Earlier work this paper cites.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018 · 2018
Earlier work this paper cites.
Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan. 2019 · 2019
Earlier work this paper cites.
Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. 2019 · 2019
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Justifying recommendations using distantly-labeled reviews and fine-grained aspects
Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019 · 2019
Earlier work this paper cites.
Deep graph infomax
Petar Velickovic, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm. 2019 · 2019
Earlier work this paper cites.
Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
Earlier work this paper cites.
Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020 · 2020
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Node feature extraction by self-supervised multi-scale neighborhood prediction
Eli Chien, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Jiong Zhang, Olgica Milenkovic, and Inderjit S Dhillon. 2021 · 2021
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, and 1 others. 2021 · 2021
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Do transformers really perform badly for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu. 2021 · 2021
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Fast multi-resolution transformer fine-tuning for extreme multi-label text classification
Jiong Zhang, Wei-Cheng Chang, Hsiang-Fu Yu, and Inderjit Dhillon. 2021 · 2021
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A note on two problems in connexion with graphs
Edsger W Dijkstra. 2022 · 2022
Cited alongside, same era.
Graphmae: Self-supervised masked graph autoencoders
Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, and Jie Tang. 2022 · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, and 1 others. 2022 · 2022
Cited alongside, same era.
Discovering language model behaviors with model-written evaluations
Ethan Perez, Sam Ringer, Kamilė Lukošiūtė, Karina Nguyen, Edwin Chen, Scott Heiner, Craig Pettit, Catherine Olsson, Sandipan Kundu, Saurav Kadavath, and 1 others. 2022 · 2022
Cited alongside, same era.
Text embeddings by weakly-supervised contrastive pre-training
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022 · 2022
Unlocking continual learning abilities in language models
Wenyu Du, Shuang Cheng, Tongxu Luo, Zihan Qiu, Zeyu Huang, Ka Chun Cheung, Reynold Cheng, and Jie Fu. 2024 · 2024
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Universal prompt tuning for graph neural networks
Taoran Fang, Yunchao Zhang, Yang Yang, Chunping Wang, and Lei Chen. 2024 · 2024
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Unigraph: Learning a cross-domain graph foundation model from natural language
Yufei He and Bryan Hooi. 2024 · 2024
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Graphalign: Pretraining one graph neural network on multiple graphs via feature alignment
Zhenyu Hou, Haozhan Li, Yukuo Cen, Jie Tang, and Yuxiao Dong. 2024 · 2024
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Can gnn be good adapter for llms?
Xuanwen Huang, Kaiqiao Han, Yang Yang, Dezheng Bao, Quanjin Tao, Ziwei Chai, and Qi Zhu. 2024b · 2024
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Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, and 1 others. 2022 · 2022
Cited alongside, same era.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, and 1 others. 2023 · 2023
Cited alongside, same era.
Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, and 1 others. 2023 · 2023
Cited alongside, same era.
Graphllm: Boosting graph reasoning ability of large language model
Ziwei Chai, Tianjie Zhang, Liang Wu, Kaiqiao Han, Xiaohai Hu, Xuanwen Huang, and Yang Yang. 2023 · 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 · 2023
Cited alongside, same era.
Talk like a graph: Encoding graphs for large language models
Bahare Fatemi, Jonathan Halcrow, and Bryan Perozzi. 2023 · 2023
Cited alongside, same era.
Explanations as features: Llm-based features for text-attributed graphs
Xiaoxin He, Xavier Bresson, Thomas Laurent, Bryan Hooi, and 1 others. 2023 · 2023
Cited alongside, same era.
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Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, and 1 others. 2024 · 2024
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On performance discrepancies across local homophily levels in graph neural networks
Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T Schaub, and Danai Koutra. 2024 · 2024
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Let your graph do the talking: Encoding structured data for llms
Bryan Perozzi, Bahare Fatemi, Dustin Zelle, Anton Tsitsulin, Mehran Kazemi, Rami Al-Rfou, and Jonathan Halcrow. 2024 · 2024
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Alopex: A computational framework for enabling on-device function calls with llms
Yide Ran, Zhaozhuo Xu, Yuhang Yao, Zijian Hu, Shanshan Han, Han Jin, Alay Dilipbhai Shah, Jipeng Zhang, Dimitris Stripelis, Tong Zhang, and 1 others. 2024 · 2024
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Opengraph: Towards open graph foundation models
Lianghao Xia, Ben Kao, and Chao Huang. 2024 · 2024
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Investigating continual pretraining in large language models: Insights and implications
Çağatay Yıldız, Nishaanth Kanna Ravichandran, Prishruit Punia, Matthias Bethge, and Beyza Ermis. 2024 · 2024
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A survey of few-shot learning on graphs: from meta-learning to pre-training and prompt learning
Xingtong Yu, Yuan Fang, Zemin Liu, Yuxia Wu, Zhihao Wen, Jianyuan Bo, Xinming Zhang, and Steven CH Hoi. 2024 · 2024
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Pre-training and prompting for few-shot node classification on text-attributed graphs
Huanjing Zhao, Beining Yang, Yukuo Cen, Junyu Ren, Chenhui Zhang, Yuxiao Dong, Evgeny Kharlamov, Shu Zhao, and Jie Tang. 2024 · 2024
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Investigating instruction tuning large language models on graphs
Kerui Zhu, Bo-Wei Huang, Bowen Jin, Yizhu Jiao, Ming Zhong, Kevin Chang, Shou-De Lin, and Jiawei Han. 2024 · 2024
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Prog: A graph prompt learning benchmark
Chenyi Zi, Haihong Zhao, Xiangguo Sun, Yiqing Lin, Hong Cheng, and Jia Li. 2024 · 2024
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Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen, and 1 others. 2025 · 2025
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Graphgpt-o: Synergistic multimodal comprehension and generation on graphs
Yi Fang, Bowen Jin, Jiacheng Shen, Sirui Ding, Qiaoyu Tan, and Jiawei Han. 2025 · 2025
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Graphit: Efficient node classification on text-attributed graphs with prompt optimized llms
Shima Khoshraftar, Niaz Abedini, and Amir Hajian. 2025 · 2025
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Grail: Graph retrieval-augmented in-context learning for node classification in real-world textual-attributed graphs
Chanuk Lim, Kyong-Ha Lee, Hyun Ji Jeong, and Sungsu Lim. 2025 · 2025
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Shengjie Ma, Chengjin Xu, Xuhui Jiang, Muzhi Li, Huaren Qu, Cehao Yang, Jiaxin Mao, and Jian Guo. 2025 · 2025
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Fidelis: Faithful reasoning in large language models for knowledge graph question answering
Yuan Sui, Yufei He, Nian Liu, Xiaoxin He, Kun Wang, and Bryan Hooi. 2025 · 2025
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Graphicl: Unlocking graph learning potential in llms through structured prompt design
Yuanfu Sun, Zhengnan Ma, Yi Fang, Jing Ma, and Qiaoyu Tan. 2025 · 2025
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A comprehensive analysis on llm-based node classification algorithms
Xixi Wu, Yifei Shen, Fangzhou Ge, Caihua Shan, Yizhu Jiao, Xiangguo Sun, and Hong Cheng. 2025 · 2025
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An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, and 1 others. 2025 · 2025
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Each graph is a new language: Graph learning with llms
Huachi Zhou, Jiahe Du, Chuang Zhou, Chang Yang, Yilin Xiao, Yuxuan Xie, and Xiao Huang. 2025 · 2025
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