Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?

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Revisiting semi-supervised learning with graph embeddings

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Attention is all you need

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Moleculenet: A benchmark for molecular machine learning

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Contrastive multi-view representation learning on graphs

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Strategies for pre-training graph neural networks

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GPT-GNN: generative pre-training of graph neural networks

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Canon: Complex analytics of network of networks for modeling adversarial activities

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Node feature extraction by self-supervised multi-scale neighborhood prediction

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Scaling up visual and vision-language representation learning with noisy text supervision

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HDMI: high-order deep multiplex infomax

Baoyu Jing, Chanyoung Park, and Hanghang Tong. 2021 · 2021

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Rethinking graph transformers with spectral attention

Devin Kreuzer, Dominique Beaini, William L. Hamilton, Vincent Létourneau, and Prudencio Tossou. 2021 · 2021

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The power of scale for parameter-efficient prompt tuning

Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021

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Prefix-tuning: Optimizing continuous prompts for generation

Xiang Lisa Li and Percy Liang. 2021 · 2021

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Learning to pre-train graph neural networks

Yuanfu Lu, Xunqiang Jiang, Yuan Fang, and Chuan Shi. 2021 · 2021

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Learning transferable visual models from natural language supervision

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021 · 2021

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Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system

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Videoclip: Contrastive pre-training for zero-shot video-text understanding

Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, and Christoph Feichtenhofer. 2021 · 2021

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Dynamic knowledge graph alignment

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Graphformers: Gnn-nested transformers for representation learning on textual graph

Junhan Yang, Zheng Liu, Shitao Xiao, Chaozhuo Li, Defu Lian, Sanjay Agrawal, Amit Singh, Guangzhong Sun, and Xing Xie. 2021 · 2021

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Deep co-attention network for multi-view subspace learning

Lecheng Zheng, Yu Cheng, Hongxia Yang, Nan Cao, and Jingrui He. 2021 · 2021

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Textgnn: Improving text encoder via graph neural network in sponsored search

Jason Zhu, Yanling Cui, Yuming Liu, Hao Sun, Xue Li, Markus Pelger, Tianqi Yang, Liangjie Zhang, Ruofei Zhang, and Huasha Zhao. 2021 · 2021

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Understanding and extending subgraph gnns by rethinking their symmetries

Fabrizio Frasca, Beatrice Bevilacqua, Michael M. Bronstein, and Haggai Maron. 2022 · 2022

Cited alongside, same era.

Meta-learned metrics over multi-evolution temporal graphs

Dongqi Fu, Liri Fang, Ross Maciejewski, Vetle I. Torvik, and Jingrui He. 2022 · 2022

Cited alongside, same era.

Learning adaptive node embeddings across graphs

Gaoyang Guo, Chaokun Wang, Bencheng Yan, Yunkai Lou, Hao Feng, Junchao Zhu, Jun Chen, Fei He, and Philip S Yu. 2022 · 2022

Cited alongside, same era.

Audioclip: Extending clip to image, text and audio

Speechclip: Integrating speech with pre-trained vision and language model

Yi-Jen Shih, Hsuan-Fu Wang, Heng-Jui Chang, Layne Berry, Hung-yi Lee, and David Harwath. 2022 · 2023

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Graph-level representation learning with joint-embedding predictive architectures

Original

Geri Skenderi, Hang Li, Jiliang Tang, and Marco Cristani. 2023 · 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. 2023a · 2023

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Virtual node tuning for few-shot node classification

Zhen Tan, Ruocheng Guo, Kaize Ding, and Huan Liu. 2023 · 2023

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Networked time series imputation via position-aware graph enhanced variational autoencoders

Dingsu Wang, Yuchen Yan, Ruizhong Qiu, Yada Zhu, Kaiyu Guan, Andrew Margenot, and Hanghang Tong. 2023 · 2023

Later among the works it cites.