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Text-attributed graphs have recently garnered significant attention due to their wide range of applications in web domains.
Language models are few-shot learners
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Distributional structure
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GraphCL: Contrastive Self-Supervised Learning of Graph Representations
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Collective classification in network data
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Mgae: Marginalized graph autoencoder for graph clustering
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Veličković, P.; Fedus, W.; Hamilton, W. L.; Liò, P.; Bengio, Y.; and Hjelm, R. D. 2018 · 2018
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Semi-supervised learning and graph neural networks for fake news detection
Benamira, A.; Devillers, B.; Lesot, E.; Ray, A. K.; Saadi, M.; and Malliaros, F. D. 2019 · 2019
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Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects
Ni, J.; Li, J.; and McAuley, J. 2019 · 2019
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Glm: General language model pretraining with autoregressive blank infilling
Du, Z.; Qian, Y.; Liu, X.; Ding, M.; Qiu, J.; Yang, Z.; and Tang, J. 2021 · 2021
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Relative and absolute location embedding for few-shot node classification on graph
Liu, Z.; Fang, Y.; Liu, C.; and Hoi, S. C. 2021 · 2021
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Mixup for node and graph classification
Wang, Y.; Wang, W.; Liang, Y.; Cai, Y.; and Hooi, B. 2021 · 2021
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GraphFormers: GNN-nested transformers for representation learning on textual graph
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Textgnn: Improving text encoder via graph neural network in sponsored search
Zhu, J.; Cui, Y.; Liu, Y.; Sun, H.; Li, X.; Pelger, M.; Yang, T.; Zhang, L.; Zhang, R.; and Zhao, H. 2021 · 2021
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Graph convolutional networks for text classification
Yao, L.; Mao, C.; and Luo, Y. 2019 · 2019
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Contrastive multi-view representation learning on graphs
Hassani, K.; and Khasahmadi, A. H. 2020 · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Hu, W.; Fey, M.; Zitnik, M.; Dong, Y.; Ren, H.; Liu, B.; Catasta, M.; and Leskovec, J. 2020 · 2020
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Graph meta learning via local subgraphs
Huang, K.; and Zitnik, M. 2020 · 2020
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Node classification on graphs with few-shot novel labels via meta transformed network embedding
Lan, L.; Wang, P.; Du, X.; Song, K.; Tao, J.; and Guan, X. 2020 · 2020
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Contextual and Non-Contextual Word Embeddings: an in-depth Linguistic Investigation
Miaschi, A.; and Dell’Orletta, F. 2020 · 2020
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Fang: Leveraging social context for fake news detection using graph representation
Nguyen, V.-H.; Sugiyama, K.; Nakov, P.; and Kan, M.-Y. 2020 · 2020
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Meta propagation networks for graph few-shot semi-supervised learning
Ding, K.; Wang, J.; Caverlee, J.; and Liu, H. 2022 · 2022
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Few-shot node classification on attributed networks with graph meta-learning
Liu, Y.; Li, M.; Li, X.; Giunchiglia, F.; Feng, X.; and Guan, R. 2022 · 2022
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Learning on large-scale text-attributed graphs via variational inference
Zhao, J.; Qu, M.; Li, C.; Yan, H.; Liu, Q.; Li, R.; Xie, X.; and Tang, J. 2022 · 2022
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Exploring the potential of large language models (llms) in learning on graphs
Chen, Z.; Mao, H.; Li, H.; Jin, W.; Wen, H.; Wei, X.; Wang, S.; Yin, D.; Fan, W.; Liu, H.; et al. 2023 · 2023
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Recommender systems in the era of large language models (llms)
Fan, W.; Zhao, Z.; Li, J.; Liu, Y.; Mei, X.; Wang, Y.; Tang, J.; and Li, Q. 2023 · 2023
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Explanations as Features: LLM-Based Features for Text-Attributed Graphs
He, X.; Bresson, X.; Laurent, T.; and Hooi, B. 2023 · 2023
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Li, J.; Liu, Y.; Fan, W.; Wei, X.-Y.; Liu, H.; Tang, J.; and Li, Q. 2023 · 2023
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Llama: Open and efficient foundation language models
Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.-A.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F.; et al. 2023 · 2023
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