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Graph Neural Networks (GNNs) have emerged as a powerful tool to capture intricate network patterns, achieving success across different domains.
Adam: A method for stochastic optimization
Diederik P Kingma. 2014 · 2014
Earlier work this paper cites.
Revisiting semi-supervised learning with graph embeddings. In International conference on machine learning . PMLR, 40–48
Zhilin Yang, William Cohen, and Ruslan Salakhudinov. 2016 · 2016
Earlier work this paper cites.
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
A Vaswani. 2017 · 2017
Earlier work this paper cites.
Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining . 974–983
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018 · 2018
Earlier work this paper cites.
Graph neural networks for social recommendation. In The world wide web conference . 417–426
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019 · 2019
Earlier work this paper cites.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
Earlier work this paper cites.
Simplifying graph convolutional networks. In International conference on machine learning . PMLR, 6861–6871
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019 · 2019
Earlier work this paper cites.
Gcc: Graph contrastive coding for graph neural network pre-training. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining . 1150–1160
Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang. 2020 · 2020
Earlier work this paper cites.
Knowing your fate: Friendship, action and temporal explanations for user engagement prediction on social apps. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining . 2269–2279
Xianfeng Tang, Yozen Liu, Neil Shah, Xiaolin Shi, Prasenjit Mitra, and Suhang Wang. 2020 · 2020
Earlier work this paper cites.
Deep graph contrastive representation learning
Yanqiao Zhu, Xu Yichen, Yu Feng, Liu Qiang, Wu Shu, and Wang Liang. 2020 · 2020
Earlier work this paper cites.
Automated self-supervised learning for graphs
Wei Jin, Xiaorui Liu, Xiangyu Zhao, Yao Ma, Neil Shah, and Jiliang Tang. 2021 · 2021
Earlier work this paper cites.
Is homophily a necessity for graph neural networks?
Yao Ma, Xiaorui Liu, Neil Shah, and Jiliang Tang. 2021 · 2021
Earlier work this paper cites.
Deep learning on graphs
Yao Ma and Jiliang Tang. 2021 · 2021
Cited alongside, same era.
Do Transformers Really Perform Badly for Graph Representation?. In Thirty-Fifth Conference on Neural Information Processing Systems
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu. 2021 · 2021
Cited alongside, same era.
An Empirical Study of Graph Contrastive Learning. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)
Yanqiao Zhu, Yichen Xu, Qiang Liu, and Shu Wu. 2021 · 2021
Cited alongside, same era.
Graphmae: Self-supervised masked graph autoencoders. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 594–604
Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, and Jie Tang. 2022 · 2022
Cited alongside, same era.
Merging models with fisher-weighted averaging
Michael S Matena and Colin A Raffel. 2022 · 2022
Cited alongside, same era.
All in One: Multi-Task Prompting for Graph Neural Networks
Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, and Jihong Guan. 2023 · 2023
Later among the works it cites.
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 · 2023
Later among the works it cites.
Better with less: A data-active perspective on pre-training graph neural networks
Jiarong Xu, Renhong Huang, Xin Jiang, Yuxuan Cao, Carl Yang, Chunping Wang, and Yang Yang. 2023 · 2023
Later among the works it cites.
LiGNN: Graph Neural Networks at LinkedIn
Fedor Borisyuk, Shihai He, Yunbo Ouyang, Morteza Ramezani, Peng Du, Xiaochen Hou, Chengming Jiang, Nitin Pasumarthy, Priya Bannur, Birjodh Tiwana, et al · 2024
Closest in time.
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GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Washington DC, USA) (KDD ’22) . Association for Computing Machinery, New York, NY, USA, 1717–1727
Mingchen Sun, Kaixiong Zhou, Xin He, Ying Wang, and Xin Wang. 2022 · 2022
Cited alongside, same era.
Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs
Zhikai Chen, Haitao Mao, Hang Li, Wei Jin, Haifang Wen, Xiaochi Wei, Shuaiqiang Wang, Dawei Yin, Wenqi Fan, Hui Liu, and Jiliang Tang. 2023 · 2023
Cited alongside, same era.
A Data-centric Framework to Endow Graph Neural Networks with Out-Of-Distribution Detection Ability. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 638–648
Yuxin Guo, Cheng Yang, Yuluo Chen, Jixi Liu, Chuan Shi, and Junping Du. 2023 · 2023
Cited alongside, same era.
PRODIGY: Enabling In-context Learning Over Graphs
Qian Huang, Hongyu Ren, Peng Chen, Gregor Kržmanc, Daniel Zeng, Percy Liang, and Jure Leskovec. 2023 · 2023
Cited alongside, same era.
Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization
Mingxuan Ju, Tong Zhao, Qianlong Wen, Wenhao Yu, Neil Shah, Yanfang Ye, and Chuxu Zhang. 2023 · 2023
Cited alongside, same era.
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. 2023a · 2023
Cited alongside, same era.
Graphprompt: Unifying pre-training and downstream tasks for graph neural networks. In Proceedings of the ACM Web Conference 2023 . 417–428
Zemin Liu, Xingtong Yu, Yuan Fang, and Xinming Zhang. 2023b · 2023
Cited alongside, same era.
Runjin Chen, Tong Zhao, Ajay Jaiswal, Neil Shah, and Zhangyang Wang. 2024b · 2024
Closest in time.
Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights
Zhikai Chen, Haitao Mao, Jingzhe Liu, Yu Song, Bingheng Li, Wei Jin, Bahare Fatemi, Anton Tsitsulin, Bryan Perozzi, Hui Liu, et al · 2024
Closest in time.
UniGraph: Learning a Cross-Domain Graph Foundation Model From Natural Language
Yufei He and Bryan Hooi. 2024 · 2024
Closest in time.
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
Closest in time.
Tackling Task Conflict and Data Imbalance through Model Merging
Mingxin Li et al · 2024
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Haitao Mao, Zhikai Chen, Wenzhuo Tang, Jianan Zhao, Yao Ma, Tong Zhao, Neil Shah, Michael Galkin, and Jiliang Tang. 2024 · 2024
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AnyGraph: Graph Foundation Model in the Wild
Lianghao Xia and Chao Huang. 2024 · 2024
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GraphAny: A Foundation Model for Node Classification on Any Graph
Jianan Zhao, Hesham Mostafa, Michael Galkin, Michael Bronstein, Zhaocheng Zhu, and Jian Tang. 2024c · 2024
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