Fetching the paper…
Reading the bibliography…
Distribution shifts on graphs -- the data distribution discrepancies between training and testing a graph machine learning model, are often ubiquitous and unavoidable in real-world scenarios.
Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2006
Earlier work this paper cites.
A unifying view on dataset shift in classification
Jose G Moreno-Torres, Troy Raeder, Rocío Alaiz-Rodríguez, Nitesh V Chawla, and Francisco Herrera · 2012
Earlier work this paper cites.
Predicting positive and negative links in signed social networks by transfer learning
Jihang Ye, Hong Cheng, Zhe Zhu, and Minghua Chen · 2013
Earlier work this paper cites.
Patterns of dataset shift
Meelis Kull and Peter Flach · 2014
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
Earlier work this paper cites.
Simplifying graph convolutional networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
Earlier work this paper cites.
Domain-adversarial graph neural networks for text classification
Man Wu, Shirui Pan, Xingquan Zhu, Chuan Zhou, and Lei Pan · 2019
Earlier work this paper cites.
Dane: Domain adaptive network embedding
Yizhou Zhang, Guojie Song, Lun Du, Shuwen Yang, and Yilun Jin · 2019
Earlier work this paper cites.
On learning invariant representations for domain adaptation
Han Zhao, Remi Tachet Des Combes, Kun Zhang, and Geoffrey Gordon · 2019
Earlier work this paper cites.
Nes-tl: Network embedding similarity-based transfer learning
Chenbo Fu, Yongli Zheng, Yi Liu, Qi Xuan, and Guanrong Chen · 2020
Earlier work this paper cites.
Diva: Domain invariant variational autoencoders
Maximilian Ilse, Jakub M Tomczak, Christos Louizos, and Max Welling · 2020
Earlier work this paper cites.
Graphon neural networks and the transferability of graph neural networks
Luana Ruiz, Luiz Chamon, and Alejandro Ribeiro · 2020
Earlier work this paper cites.
Adversarial deep network embedding for cross-network node classification
Xiao Shen, Quanyu Dai, Fu-lai Chung, Wei Lu, and Kup-Sze Choi · 2020
Earlier work this paper cites.
Network together: Node classification via cross-network deep network embedding
Xiao Shen, Quanyu Dai, Sitong Mao, Fu-lai Chung, and Kup-Sze Choi · 2020
Earlier work this paper cites.
Unsupervised domain adaptive graph convolutional networks
Man Wu, Shirui Pan, Chuan Zhou, Xiaojun Chang, and Xingquan Zhu · 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
Earlier work this paper cites.
From local structures to size generalization in graph neural networks
Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik, and Haggai Maron · 2021
Earlier work this paper cites.
Adversarial separation network for cross-network node classification
Xiaowen Zhang, Yuntao Du, Rongbiao Xie, and Chongjun Wang · 2021
Earlier work this paper cites.
Shift-robust gnns: Overcoming the limitations of localized graph training data
Qi Zhu, Natalia Ponomareva, Jiawei Han, and Bryan Perozzi · 2021
Earlier work this paper cites.
Transfer learning of graph neural networks with ego-graph information maximization
Qi Zhu, Carl Yang, Yidan Xu, Haonan Wang, Chao Zhang, and Jiawei Han · 2021
Earlier work this paper cites.
Graphtta: Test time adaptation on graph neural networks
Guanzi Chen, Jiying Zhang, Xi Xiao, and Yang Li · 2022
Cited alongside, same era.
Learning causally invariant representations for out-of-distribution generalization on graphs
Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, MA Kaili, Binghui Xie, Tongliang Liu, Bo Han, and James Cheng · 2022
Cited alongside, same era.
Tree mover’s distance: Bridging graph metrics and stability of graph neural networks
Ching-Yao Chuang and Stefanie Jegelka · 2022
Cited alongside, same era.
Graph transfer learning via adversarial domain adaptation with graph convolution
Quanyu Dai, Xiao-Ming Wu, Jiaren Xiao, Xiao Shen, and Dan Wang · 2022
Cited alongside, same era.
Fakeedge: Alleviate dataset shift in link prediction
Kaiwen Dong, Yijun Tian, Zhichun Guo, Yang Yang, and Nitesh Chawla · 2022
Cited alongside, same era.
Source-free unsupervised domain adaptation: A survey
Learning adaptive node embeddings across graphs
Gaoyang Guo, Chaokun Wang, Bencheng Yan, Yunkai Lou, Hao Feng, Junchao Zhu, Jun Chen, Fei He, and Philip Yu · 2023
Later among the works it cites.
Empowering graph representation learning with test-time graph transformation
Wei Jin, Tong Zhao, Jiayuan Ding, Yozen Liu, Jiliang Tang, and Neil Shah · 2023
Later among the works it cites.
Graphpatcher: Mitigating degree bias for graph neural networks via test-time augmentation
Mingxuan Ju, Tong Zhao, Wenhao Yu, Neil Shah, and Yanfang Ye · 2023
Later among the works it cites.
A comprehensive survey on test-time adaptation under distribution shifts
Jian Liang, Ran He, and Tieniu Tan · 2023
Later among the works it cites.
Structural re-weighting improves graph domain adaptation
Shikun Liu, Tianchun Li, Yongbin Feng, Nhan Tran, Han Zhao, Qiang Qiu, and Pan Li · 2023
Later among the works it cites.
Semi-supervised domain adaptation in graph transfer learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yuqi Fang, Pew-Thian Yap, Weili Lin, Hongtu Zhu, and Mingxia Liu · 2022
Cited alongside, same era.
Fair node representation learning via adaptive data augmentation
O Deniz Kose and Yanning Shen · 2022
Cited alongside, same era.
Out-of-distribution generalization on graphs: A survey
Haoyang Li, Xin Wang, Ziwei Zhang, and Wenwu Zhu · 2022
Cited alongside, same era.
Confidence may cheat: Self-training on graph neural networks under distribution shift
Hongrui Liu, Binbin Hu, Xiao Wang, Chuan Shi, Zhiqiang Zhang, and Jun Zhou · 2022
Cited alongside, same era.
Test-time training for graph neural networks
Yiqi Wang, Chaozhuo Li, Wei Jin, Rui Li, Jianan Zhao, Jiliang Tang, and Xing Xie · 2022
Cited alongside, same era.
Reinforced sample selection for graph neural networks transfer learning
Bo Wu, Xun Liang, Xiangping Zheng, Jun Wang, and Xiaoping Zhou · 2022
Cited alongside, same era.
Knowledge distillation improves graph structure augmentation for graph neural networks
Lirong Wu, Haitao Lin, Yufei Huang, and Stan Z Li · 2022
Cited alongside, same era.
Ziyue Qiao, Xiao Luo, Meng Xiao, Hao Dong, Yuanchun Zhou, and Hui Xiong · 2023
Later among the works it cites.
Improving graph domain adaptation with network hierarchy
Boshen Shi, Yongqing Wang, Fangda Guo, Jiangli Shao, Huawei Shen, and Xueqi Cheng · 2023
Later among the works it cites.
Graph prompt learning: A comprehensive survey and beyond
Xiangguo Sun, Jiawen Zhang, Xixi Wu, Hong Cheng, Yun Xiong, and Jia Li · 2023
Later among the works it cites.
Non-iid transfer learning on graphs
Jun Wu, Jingrui He, and Elizabeth Ainsworth · 2023
Later among the works it cites.
Graph domain adaptation via theory-grounded spectral regularization
Yuning You, Tianlong Chen, Zhangyang Wang, and Yang Shen · 2023
Later among the works it cites.
A comprehensive survey on source-free domain adaptation
Zhiqi Yu, Jingjing Li, Zhekai Du, Lei Zhu, and Heng Tao Shen · 2023
Later among the works it cites.
Adding conditional control to text-to-image diffusion models
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala · 2023
Later among the works it cites.
Graphglow: Universal and generalizable structure learning for graph neural networks
Wentao Zhao, Qitian Wu, Chenxiao Yang, and Junchi Yan · 2023
Later among the works it cites.
Gnnevaluator: Evaluating gnn performance on unseen graphs without labels
Xin Zheng, Miao Zhang, Chunyang Chen, Soheila Molaei, Chuan Zhou, and Shirui Pan · 2023
Later among the works it cites.
Explaining and adapting graph conditional shift
Qi Zhu, Yizhu Jiao, Natalia Ponomareva, Jiawei Han, and Bryan Perozzi · 2023
Later among the works it cites.
Graphcontrol: Adding conditional control to universal graph pre-trained models for graph domain transfer learning
Yun Zhu, Yaoke Wang, Haizhou Shi, Zhenshuo Zhang, and Siliang Tang · 2023
Later among the works it cites.
Graph domain adaptation: A generative view
Ruichu Cai, Fengzhu Wu, Zijian Li, Pengfei Wei, Lingling Yi, and Kun Zhang · 2024
Closest in time.
Source free unsupervised graph domain adaptation
Haitao Mao, Lun Du, Yujia Zheng, Qiang Fu, Zelin Li, Xu Chen, Shi Han, and Dongmei Zhang · 2024
Closest in time.
Distribution consistency based self-training for graph neural networks with sparse labels
Fali Wang, Tianxiang Zhao, and Suhang Wang · 2024
Closest in time.
Online gnn evaluation under test-time graph distribution shifts
Xin Zheng, Dongjin Song, Qingsong Wen, Bo Du, and Shirui Pan · 2024
Closest in time.