Fetching the paper…
Reading the bibliography…
Large Language Models (LLMs) have revolutionized the fields of computer vision (CV) and natural language processing (NLP).
Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008 · 2008
Earlier work this paper cites.
Query-driven active surveying for collective classification. In 10th international workshop on mining and learning with graphs , Vol. 8. 1
Galileo Namata, Ben London, Lise Getoor, Bert Huang, and U Edu. 2012 · 2012
Earlier work this paper cites.
Image-based recommendations on styles and substitutes. In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval . 43–52
Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015 · 2015
Earlier work this paper cites.
Variational graph auto-encoders
Thomas N Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations
Thomas N. Kipf and Max Welling. 2017 · 2017
Earlier work this paper cites.
Transfer learning for deep learning on graph-structured data. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 31
Jaekoo Lee, Hyunjae Kim, Jongsun Lee, and Sungroh Yoon. 2017 · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann. 2018 · 2018
Earlier work this paper cites.
Petar Veličković, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm. 2018b · 2018
Earlier work this paper cites.
Geom-GCN: Geometric Graph Convolutional Networks. In International Conference on Learning Representations
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2019 · 2019
Earlier work this paper cites.
Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang. 2019 · 2019
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations. In International conference on machine learning . PMLR, 1597–1607
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020 · 2020
Earlier work this paper cites.
Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
Earlier work this paper cites.
Contrastive multi-view representation learning on graphs. In International conference on machine learning . PMLR, 4116–4126
Kaveh Hassani and Amir Hosein Khasahmadi. 2020 · 2020
Earlier work this paper cites.
Self-supervised learning on graphs: Deep insights and new direction
Wei Jin, Tyler Derr, Haochen Liu, Yiqi Wang, Suhang Wang, Zitao Liu, and Jiliang Tang. 2020 · 2020
Earlier work this paper cites.
Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes. In Proceedings of the AAAI conference on artificial intelligence , Vol. 34. 5892–5899
Ke Sun, Zhouchen Lin, and Zhanxing Zhu. 2020 · 2020
Earlier work this paper cites.
Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020 · 2020
Earlier work this paper cites.
A comprehensive survey on transfer learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He. 2020 · 2020
Cited alongside, same era.
Beyond low-frequency information in graph convolutional networks. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. 3950–3957
Deyu Bo, Xiao Wang, Chuan Shi, and Huawei Shen. 2021 · 2021
Cited alongside, same era.
All nlp tasks are generation tasks: A general pretraining framework
Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, and Jie Tang. 2021 · 2021
Cited alongside, same era.
Imagenet-21k pretraining for the masses
Tal Ridnik, Emanuel Ben-Baruch, Asaf Noy, and Lihi Zelnik-Manor. 2021 · 2021
Cited alongside, same era.
Prompt tuning for graph neural networks
Taoran Fang, Yunchao Zhang, Yang Yang, and Chunping Wang. 2022 · 2022
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
Later among the works it cites.
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023c · 2023
Later among the works it cites.
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
Later among the works it cites.
When Do Graph Neural Networks Help with Node Classification? Investigating the Homophily Principle on Node Distinguishability. In Thirty-seventh Conference on Neural Information Processing Systems
Sitao Luan, Chenqing Hua, Minkai Xu, Qincheng Lu, Jiaqi Zhu, Xiao-Wen Chang, Jie Fu, Jure Leskovec, and Doina Precup. 2023 · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Community question answering entity linking via leveraging auxiliary data
Yuhan Li, Wei Shen, Jianbo Gao, and Yadong Wang. 2022 · 2022
Cited alongside, same era.
Graph self-supervised learning: A survey
Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, and S Yu Philip. 2022 · 2022
Cited alongside, same era.
Rethinking graph neural networks for anomaly detection. In International Conference on Machine Learning . PMLR, 21076–21089
Jianheng Tang, Jiajin Li, Ziqi Gao, and Jia Li. 2022 · 2022
Cited alongside, same era.
Simgrace: A simple framework for graph contrastive learning without data augmentation. In Proceedings of the ACM Web Conference 2022 . 1070–1079
Jun Xia, Lirong Wu, Jintao Chen, Bozhen Hu, and Stan Z Li. 2022 · 2022
Cited alongside, same era.
A survey on negative transfer
Wen Zhang, Lingfei Deng, Lei Zhang, and Dongrui Wu. 2022 · 2022
Cited alongside, same era.
Rosa: a robust self-aligned framework for node-node graph contrastive learning
Yun Zhu, Jianhao Guo, Fei Wu, and Siliang Tang. 2022 · 2022
Cited alongside, same era.
Xiangguo Sun, Jiawen Zhang, Xixi Wu, Hong Cheng, Yun Xiong, and Jia Li. 2023b · 2023
Later among the works it cites.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Later among the works it cites.
A survey of graph prompting methods: techniques, applications, and challenges
Xuansheng Wu, Kaixiong Zhou, Mingchen Sun, Xin Wang, and Ninghao Liu. 2023 · 2023
Later among the works it cites.
Node classification beyond homophily: Towards a general solution. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 2862–2873
Zhe Xu, Yuzhong Chen, Qinghai Zhou, Yuhang Wu, Menghai Pan, Hao Yang, and Hanghang Tong. 2023 · 2023
Later among the works it cites.
Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning
Yihua Zhang, Yimeng Zhang, Aochuan Chen, Jinghan Jia, Jiancheng Liu, Gaowen Liu, Mingyi Hong, Shiyu Chang, and Sijia Liu. 2023 · 2023
Later among the works it cites.
Effective fault scenario identification for communication networks via knowledge-enhanced graph neural networks
Haihong Zhao, Bo Yang, Jiaxu Cui, Qianli Xing, Jiaxing Shen, Fujin Zhu, and Jiannong Cao. 2023 · 2023
Later among the works it cites.
GraphWiz: An Instruction-Following Language Model for Graph Problems
Nuo Chen, Yuhan Li, Jianheng Tang, and Jia Li. 2024 · 2024
Closest in time.
iGraphMix: Input Graph Mixup Method for Node Classification. In The Twelfth International Conference on Learning Representations
Jongwon Jeong, Hoyeop Lee, Hyui Geon Yoon, Beomyoung Lee, Junhee Heo, Geonsoo Kim, and Jin Seon Kim. 2024 · 2024
Closest in time.
ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs
Yuhan Li, Peisong Wang, Zhixun Li, Jeffrey Xu Yu, and Jia Li. 2024 · 2024
Closest in time.
SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases. In The Twelfth International Conference on Learning Representations
Yang Liu, Jiashun Cheng, Haihong Zhao, Tingyang Xu, Peilin Zhao, Fugee Tsung, Jia Li, and Yu Rong. 2024 · 2024
Closest in time.
Large language models can learn temporal reasoning
Siheng Xiong, Ali Payani, Ramana Kompella, and Faramarz Fekri. 2024 · 2024
Closest in time.
Weakly Supervised Anomaly Detection via Knowledge-Data Alignment. In Proceedings of the ACM on Web Conference 2024 . 4083–4094
Haihong Zhao, Chenyi Zi, Yang Liu, Chen Zhang, Yan Zhou, and Jia Li. 2024 · 2024
Closest in time.