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Few-shot Node Classification (FSNC) is a challenge in graph representation learning where only a few labeled nodes per class are available for training.
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Inductive Representation Learning on Large Graphs. In NeurIPS . 1024–1034
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Prototypical networks for few-shot learning. In NeurIPS
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Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking. In ICLR
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Few-shot Classification on Graphs with Structural Regularized GCNs. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence
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Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds
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Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
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Huggingface’s transformers: State-of-the-art natural language processing
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How powerful are graph neural networks?. In Proceedings of the 2019 International Conference on Learning Representations
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019 · 2019
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Meta-gnn: On few-shot node classification in graph meta-learning. In CIKM
Fan Zhou, Chengtai Cao, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, and Ji Geng. 2019 · 2019
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Graph prototypical networks for few-shot learning on attributed networks. In CIKM
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An image is worth 16x16 words: Transformers for image recognition at scale
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Contrastive multi-view representation learning on graphs. In International Conference on Machine Learning . PMLR, 4116–4126
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Heterogeneous graph transformer. In Proceedings of The Web Conference 2020 . 2704–2710
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Graph meta learning via local subgraphs. In NeurIPS
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Sub-graph contrast for scalable self-supervised graph representation learning. In 2020 IEEE international conference on data mining (ICDM) . IEEE, 222–231
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Graphit: Encoding graph structure in transformers
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Neural Link Prediction with Walk Pooling. In International Conference on Learning Representations
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Bootstrapped Representation Learning on Graphs. In ICLR Workshop on Geometrical and Topological Representation Learning
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Remi Munos, Petar Veličković, and Michal Valko. 2021 · 2021
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Node classification on graphs with few-shot novel labels via meta transformed network embedding
Lin Lan, Pinghui Wang, Xuefeng Du, Kaikai Song, Jing Tao, and Xiaohong Guan. 2020 · 2020
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Self-supervised graph transformer on large-scale molecular data
Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang. 2020 · 2020
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Microsoft academic graph: When experts are not enough
Kuansan Wang, Zhihong Shen, Chiyuan Huang, Chieh-Han Wu, Yuxiao Dong, and Anshul Kanakia. 2020b · 2020
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Graph few-shot learning via knowledge transfer. In Proceedings of the 34th AAAI Conference on Artificial Intelligence
Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh Chawla, and Zhenhui Li. 2020 · 2020
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Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020 · 2020
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Graph-bert: Only attention is needed for learning graph representations
Jiawei Zhang, Haopeng Zhang, Congying Xia, and Li Sun. 2020 · 2020
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Deep graph contrastive representation learning
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2020 · 2020
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Zhihao Wen, Yuan Fang, and Zemin Liu. 2021 · 2021
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Structure-aware transformer for graph representation learning. In International Conference on Machine Learning . PMLR, 3469–3489
Dexiong Chen, Leslie O’Bray, and Karsten Borgwardt. 2022 · 2022
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Data augmentation for deep graph learning: A survey
Kaize Ding, Zhe Xu, Hanghang Tong, and Huan Liu. 2022 · 2022
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Prompt Tuning for Graph Neural Networks
Taoran Fang, Yunchao Zhang, Yang Yang, and Chunping Wang. 2022 · 2022
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Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim. 2022 · 2022
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Simple unsupervised graph representation learning. AAAI
Yujie Mo, Liang Peng, Jie Xu, Xiaoshuang Shi, and Xiaofeng Zhu. 2022 · 2022
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Recipe for a general, powerful, scalable graph transformer
Ladislav Rampášek, Michael Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini. 2022 · 2022
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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 . 1717–1727
Mingchen Sun, Kaixiong Zhou, Xin He, Ying Wang, and Xin Wang. 2022 · 2022
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A Simple Yet Effective Pretraining Strategy for Graph Few-shot Learning
Zhen Tan, Kaize Ding, Ruocheng Guo, and Huan Liu. 2022b · 2022
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Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification
Zhen Tan, Song Wang, Kaize Ding, Jundong Li, and Huan Liu. 2022c · 2022
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Task-Adaptive Few-shot Node Classification
Song Wang, Kaize Ding, Chuxu Zhang, Chen Chen, and Jundong Li. 2022 · 2022
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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. 2023 · 2023
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