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Learning on Graphs has attracted immense attention due to its wide real-world applications.
Distributional structure
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Automating the construction of internet portals with machine learning
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Collective classification in network data
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Efficient estimation of word representations in vector space
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Unravelling graph-exchange file formats
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Revisiting semi-supervised learning with graph embeddings
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Neural message passing for quantum chemistry
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Inductive representation learning on large graphs
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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Graph attention networks
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2018
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Graph convolutional networks for text classification
L. Yao, C. Mao, and Y. Luo · 2018
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Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
W.-L. Chiang, X. Liu, S. Si, Y. Li, S. Bengio, and C.-J. Hsieh · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2019
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How contextual are contextualized word representations? Comparing the geometry of BERT, ELMo, and GPT-2 embeddings
K. Ethayarajh · 2019
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Fast graph representation learning with pytorch geometric
M. Fey and J. E. Lenssen · 2019
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K-bert: Enabling language representation with knowledge graph
W. Liu, P. Zhou, Z. Zhao, Z. Wang, Q. Ju, H. Deng, and P. Wang · 2019
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Language models as knowledge bases?
F. Petroni, T. Rocktäschel, P. Lewis, A. Bakhtin, Y. Wu, A. H. Miller, and S. Riedel · 2019
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Language models are unsupervised multitask learners
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Sentence-BERT: Sentence embeddings using Siamese BERT-networks
N. Reimers and I. Gurevych · 2019
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Ernie: Enhanced representation through knowledge integration
Y. Sun, S. Wang, Y. Li, S. Feng, X. Chen, H. Zhang, X. Tian, D. Zhu, H. Tian, and H. Wu · 2019
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Deep graph library: Towards efficient and scalable deep learning on graphs
M. Wang, L. Yu, D. Zheng, Q. Gan, Y. Gai, Z. Ye, M. Li, J. Zhou, Q. Huang, C. Ma, Z. Huang, Q. Guo, H. Zhang, H. Lin, J. J. Zhao, J. Li, A. Smola, and Z. Zhang · 2019
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Huggingface’s transformers: State-of-the-art natural language processing
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Active learning for graph neural networks via node feature propagation
Y. Wu, Y. Xu, A. Singh, Y. Yang, and A. W. Dubrawski · 2019
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Deberta: Decoding-enhanced bert with disentangled attention
P. He, X. Liu, J. Gao, and W. Chen · 2020
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Open graph benchmark: Datasets for machine learning on graphs
W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, and J. Leskovec · 2020
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Gpt-gnn: Generative pre-training of graph neural networks
Z. Hu, Y. Dong, K. Wang, K.-W. Chang, and Y. Sun · 2020
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Fine-grained fact verification with kernel graph attention network
Z. Liu, C. Xiong, M. Sun, and Z. Liu · 2020
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Contextual and non-contextual word embeddings: an in-depth linguistic investigation
A. Miaschi and F. Dell’Orletta · 2020
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Pre-trained models for natural language processing: A survey
X. Qiu, T. Sun, Y. Xu, Y. Shao, N. Dai, and X. Huang · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu · 2020
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Beyond homophily in graph neural networks: Current limitations and effective designs
J. Zhu, Y. Yan, L. Zhao, M. Heimann, L. Akoglu, and D. Koutra · 2020
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Nrgnn: Learning a label noise resistant graph neural network on sparsely and noisily labeled graphs
E. Dai, C. Aggarwal, and S. Wang · 2021
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Training graph neural networks with 1000 layers
G. Li, M. Müller, B. Ghanem, and V. Koltun · 2021
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Selection-inference: Exploiting large language models for interpretable logical reasoning
A. Creswell, M. Shanahan, and I. Higgins · 2023
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Simteg: A frustratingly simple approach improves textual graph learning
K. Duan, Q. Liu, T.-S. Chua, S. Yan, W. T. Ooi, Q. Xie, and J. He · 2023
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Chat-rec: Towards interactive and explainable llms-augmented recommender system
Y. Gao, T. Sheng, Y. Xiang, Y. Xiong, H. Wang, and J. Zhang · 2023
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J. Guo, L. Du, and H. Liu · 2023
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Explanations as features: Llm-based features for text-attributed graphs
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Y. Ma and J. Tang · 2021
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Scalable and adaptive graph neural networks with self-label-enhanced training
C. Sun, H. Gu, and J. Hu · 2021
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Graph learning: A survey
F. Xia, K. Sun, S. Yu, A. Aziz, L. Wan, S. Pan, and H. Liu · 2021
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Graphformers: GNN-nested transformers for representation learning on textual graph
J. Yang, Z. Liu, S. Xiao, C. Li, D. Lian, S. Agrawal, A. S, G. Sun, and X. Xie · 2021
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Textgnn: Improving text encoder via graph neural network in sponsored search
J. Zhu, Y. Cui, Y. Liu, H. Sun, X. Li, M. Pelger, L. Zhang, T. Yan, R. Zhang, and H. Zhao · 2021
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Node feature extraction by self-supervised multi-scale neighborhood prediction
E. Chien, W.-C. Chang, C.-J. Hsieh, H.-F. Yu, J. Zhang, O. Milenkovic, and I. S. Dhillon · 2022
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Long range graph benchmark
V. P. Dwivedi, L. Rampášek, M. Galkin, A. Parviz, G. Wolf, A. T. Luu, and D. Beaini · 2022
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X. He, X. Bresson, T. Laurent, and B. Hooi · 2023
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Can llms effectively leverage graph structural information: When and why
J. Huang, X. Zhang, Q. Mei, and J. Ma · 2023
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Y. Ji, Y. Gong, Y. Peng, C. Ni, P. Sun, D. Pan, B. Ma, and X. Li · 2023
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J. Li, Y. Liu, W. Fan, X. Wei, H. Liu, J. Tang, and Q. Li · 2023
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Informative pseudo-labeling for graph neural networks with few labels
Y. Li, J. Yin, and L. Chen · 2023
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One for all: Towards training one graph model for all classification tasks
H. Liu, J. Feng, L. Kong, N. Liang, D. Tao, Y. Chen, and M. Zhang · 2023
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Is chatgpt a good recommender? a preliminary study
J. Liu, C. Liu, R. Lv, K. Zhou, and Y. Zhang · 2023
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Demystifying structural disparity in graph neural networks: Can one size fit all?
H. Mao, Z. Chen, W. Jin, H. Han, Y. Ma, T. Zhao, N. Shah, and J. Tang · 2023
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MTEB: Massive text embedding benchmark
N. Muennighoff, N. Tazi, L. Magne, and N. Reimers · 2023
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OpenAI · 2023
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Frozen transformers in language models are effective visual encoder layers
Z. Pang, Z. Xie, Y. Man, and Y.-X. Wang · 2023
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Disentangled representation learning with large language models for text-attributed graphs
Y. Qin, X. Wang, Z. Zhang, and W. Zhu · 2023
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Toolformer: Language models can teach themselves to use tools
T. Schick, J. Dwivedi-Yu, R. Dessì, R. Raileanu, M. Lomeli, L. Zettlemoyer, N. Cancedda, and T. Scialom · 2023
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Text classification via large language models
X. Sun, X. Li, J. Li, F. Wu, S. Guo, T. Zhang, and G. Wang · 2023
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Graphgpt: Graph instruction tuning for large language models
J. Tang, Y. Yang, W. Wei, L. Shi, L. Su, S. Cheng, D. Yin, and C. Huang · 2023
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Llama: Open and efficient foundation language models
H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, et al · 2023
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Can language models solve graph problems in natural language?
H. Wang, S. Feng, T. He, Z. Tan, X. Han, and Y. Tsvetkov · 2023
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Graph neural architecture search with gpt-4
H. Wang, Y. Gao, X. Zheng, P. Zhang, H. Chen, and J. Bu · 2023
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On the robustness of chatgpt: An adversarial and out-of-distribution perspective
J. Wang, X. Hu, W. Hou, H. Chen, R. Zheng, Y. Wang, L. Yang, H. Huang, W. Ye, X. Geng, et al · 2023
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Natural language is all a graph needs
R. Ye, C. Zhang, R. Wang, S. Xu, and Y. Zhang · 2023
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Graph-toolformer: To empower llms with graph reasoning ability via prompt augmented by chatgpt
J. Zhang · 2023
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Llm4dyg: Can large language models solve problems on dynamic graphs?
Z. Zhang, X. Wang, Z. Zhang, H. Li, Y. Qin, S. Wu, and W. Zhu · 2023
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Learning on large-scale text-attributed graphs via variational inference
J. Zhao, M. Qu, C. Li, H. Yan, Q. Liu, R. Li, X. Xie, and J. Tang · 2023
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Graphtext: Graph reasoning in text space
J. Zhao, L. Zhuo, Y. Shen, M. Qu, K. Liu, M. Bronstein, Z. Zhu, and J. Tang · 2023
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A survey of large language models
W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong, Y. Du, C. Yang, Y. Chen, Z. Chen, J. Jiang, R. Ren, Y. Li, X. Tang, Z. Liu, P. Liu, J. Nie, and J. rong Wen · 2023
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