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Zero-shot graph machine learning, especially with graph neural networks (GNNs), has garnered significant interest due to the challenge of scarce labeled data.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Earlier work this paper cites.
FastGCN: Fast learning with graph convolutional networks via importance sampling
Jie Chen, Tengfei Ma, and Cao Xiao · 2018
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Large-scale learnable graph convolutional networks
Hongyang Gao, Zhengyang Wang, and Shuiwang Ji · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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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
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Predicting path failure in time-evolving graphs
Jia Li, Zhichao Han, Hong Cheng, Jiao Su, Pengyun Wang, Jianfeng Zhang, and Lujia Pan · 2019
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Deep graph infomax
Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Graph transformer networks
Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J Kim · 2019
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Contrastive multi-view representation learning on graphs
Kaveh Hassani and Amir Hosein Khasahmadi · 2020
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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
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Gcc: Graph contrastive coding for graph neural network pre-training
Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang · 2020
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L2-gcn: Layer-wise and learned efficient training of graph convolutional networks
Y. You, T. Chen, Z. Wang, and Y. Shen · 2020
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Deep graph contrastive representation learning
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang · 2020
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Multi-level hyperedge distillation for social linking prediction on sparsely observed networks
Xiangguo Sun, Hongzhi Yin, Bo Liu, Hongxu Chen, Qing Meng, Wang Han, and Jiuxin Cao · 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
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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
Cited alongside, same era.
Graph contrastive learning automated
Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang · 2021
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Graph contrastive learning with adaptive augmentation
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang · 2021
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Graphmae: Self-supervised masked graph autoencoders
Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, and Jie Tang · 2022
Cited alongside, same era.
Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V Le · 2022
Cited alongside, same era.
Evaluating large language models on graphs: Performance insights and comparative analysis
Chang Liu and Bo Wu · 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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All in one: Multi-task prompting for graph neural networks
Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, and Jihong Guan · 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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Qitian Wu, Wentao Zhao, Zenan Li, David Wipf, and Junchi Yan · 2022
Cited alongside, same era.
Simgrace: A simple framework for graph contrastive learning without data augmentation
Jun Xia, Lirong Wu, Jintao Chen, Bozhen Hu, and Stan Z. Li · 2022
Cited alongside, same era.
Geometric knowledge distillation: Topology compression for graph neural networks
Chenxiao Yang, Qitian Wu, and Junchi Yan · 2022
Cited alongside, same era.
Graph-less neural networks: Teaching old MLPs new tricks via distillation
Shichang Zhang, Yozen Liu, Yizhou Sun, and Neil Shah · 2022
Cited alongside, same era.
Exploring the potential of large language models (LLMs) in learning on graph
Zhikai Chen, Haitao Mao, Hang Li, Wei Jin, Hongzhi Wen, Xiaochi Wei, Shuaiqiang Wang, Dawei Yin, Wenqi Fan, Hui Liu, and Jiliang Tang · 2023
Cited alongside, same era.
Wiener graph deconvolutional network improves graph self-supervised learning
Jiashun Cheng, Man Li, Jia Li, and Fugee Tsung · 2023
Cited alongside, same era.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, 2023
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing · 2023
Cited alongside, same era.
Augmenting low-resource text classification with graph-grounded pre-training and prompting
Zhihao Wen and Yuan Fang · 2023
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DIFFormer: Scalable (graph) transformers induced by energy constrained diffusion
Qitian Wu, Chenxiao Yang, Wentao Zhao, Yixuan He, David Wipf, and Junchi Yan · 2023
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A comprehensive study on text-attributed graphs: Benchmarking and rethinking
Hao Yan, Chaozhuo Li, Ruosong Long, Chao Yan, Jianan Zhao, Wenwen Zhuang, Jun Yin, Peiyan Zhang, Weihao Han, Hao Sun, Weiwei Deng, Qi Zhang, Lichao Sun, Xing Xie, and Senzhang Wang · 2023
Later among the works it cites.
Natural language is all a graph needs
Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu, and Yongfeng Zhang · 2023
Later among the works it cites.
Empower text-attributed graphs learning with large language models (llms)
Jianxiang Yu, Yuxiang Ren, Chenghua Gong, Jiaqi Tan, Xiang Li, and Xuecang Zhang · 2023
Later among the works it cites.
Harnessing explanations: LLM-to-LM interpreter for enhanced text-attributed graph representation learning
Xiaoxin He, Xavier Bresson, Thomas Laurent, Adam Perold, Yann LeCun, and Bryan Hooi · 2024
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Unigraph: Learning a cross-domain graph foundation model from natural language
Yufei He and Bryan Hooi · 2024
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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 · 2024
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TEST: Text prototype aligned embedding to activate LLM’s ability for time series
Chenxi Sun, Hongyan Li, Yaliang Li, and Shenda Hong · 2024
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Opengraph: Towards open graph foundation models
Lianghao Xia, Ben Kao, and Chao Huang · 2024
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Graphtranslator: Aligning graph model to large language model for open-ended tasks
Mengmei Zhang, Mingwei Sun, Peng Wang, Shen Fan, Yanhu Mo, Xiaoxiao Xu, Hong Liu, Cheng Yang, and Chuan Shi · 2024
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