Fetching the paper…
Reading the bibliography…
The issue of distribution shifts is emerging as a critical concern in graph representation learning.
On estimation of a probability density function and mode
Emanuel Parzen · 1962
Earlier work this paper cites.
Monte carlo simulation in statistical physics
Kurt Binder, Dieter Heermann, Lyle Roelofs, A John Mallinckrodt, and Susan McKay · 1993
Earlier work this paper cites.
Covariate shift adaptation by importance weighted cross validation
Masashi Sugiyama, Matthias Krauledat, and Klaus-Robert Müller · 2007
Earlier work this paper cites.
Domain generalization via invariant feature representation
Krikamol Muandet, David Balduzzi, and Bernhard Schölkopf · 2013
Earlier work this paper cites.
Generating images with perceptual similarity metrics based on deep networks
Alexey Dosovitskiy and Thomas Brox · 2016
Earlier work this paper cites.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Earlier work this paper cites.
Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein · 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.
Moleculenet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 2018
Earlier work this paper cites.
Certifying some distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, and John Duchi · 2018
Earlier work this paper cites.
Generalizing to unseen domains via adversarial data augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C Duchi, Vittorio Murino, and Silvio Savarese · 2018
Earlier work this paper cites.
Minimax statistical learning with wasserstein distances
Jaeho Lee and Maxim Raginsky · 2018
Earlier work this paper cites.
Causally regularized learning with agnostic data selection bias
Zheyan Shen, Peng Cui, Kun Kuang, Bo Li, and Peixuan Chen · 2018
Earlier work this paper cites.
Attention-based graph neural network for semi-supervised learning
Kiran K Thekumparampil, Chong Wang, Sewoong Oh, and Li-Jia Li · 2018
Earlier work this paper cites.
Invariant models for causal transfer learning
Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner, and Jonas Peters · 2018
Earlier work this paper cites.
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
Earlier work this paper cites.
Distributionally robust optimization: A review
Hamed Rahimian and Sanjay Mehrotra · 2019
Earlier work this paper cites.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Earlier work this paper cites.
Invariant rationalization
Shiyu Chang, Yang Zhang, Mo Yu, and Tommi Jaakkola · 2020
Earlier work this paper cites.
Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
Earlier work this paper cites.
Dropedge: Towards deep graph convolutional networks on node classification
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang · 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
Earlier work this paper cites.
Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2020
Earlier work this paper cites.
Rethinking importance weighting for deep learning under distribution shift
Tongtong Fang, Nan Lu, Gang Niu, and Masashi Sugiyama · 2020
Earlier work this paper cites.
Simple and deep graph convolutional networks
Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li · 2020
Earlier work this paper cites.
Tudataset: A collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann · 2020
Earlier work this paper cites.
When is invariance useful in an out-of-distribution generalization problem?
Masanori Koyama and Shoichiro Yamaguchi · 2020
Earlier work this paper cites.
Invariant risk minimization games
Kartik Ahuja, Karthikeyan Shanmugam, Kush Varshney, and Amit Dhurandhar · 2020
Earlier work this paper cites.
Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann · 2020
Earlier work this paper cites.
Learning to extrapolate knowledge: Transductive few-shot out-of-graph link prediction
Jinheon Baek, Dong Bok Lee, and Sung Ju Hwang · 2020
Earlier work this paper cites.
Towards recognizing unseen categories in unseen domains
Massimiliano Mancini, Zeynep Akata, Elisa Ricci, and Barbara Caputo · 2020
Cited alongside, same era.
Towards out-of-distribution generalization: A survey
Zheyan Shen, Jiashuo Liu, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, and Peng Cui · 2021
Cited alongside, same era.
Deep stable learning for out-of-distribution generalization
Xingxuan Zhang, Peng Cui, Renzhe Xu, Linjun Zhou, Yue He, and Zheyan Shen · 2021
Cited alongside, same era.
Mixup for node and graph classification
Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, and Bryan Hooi · 2021
Cited alongside, same era.
Adversarial graph augmentation to improve graph contrastive learning
Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville · 2021
Cited alongside, same era.
Out-of-distribution generalization via risk extrapolation (rex)
Graph data augmentation for graph machine learning: A survey
Tong Zhao, Gang Liu, Stephan Günnemann, and Meng Jiang · 2022
Closest in time.
Robust optimization as data augmentation for large-scale graphs
Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, and Tom Goldstein · 2022
Closest in time.
Learning invariant graph representations for out-of-distribution generalization
Haoyang Li, Ziwei Zhang, Xin Wang, and Wenwu Zhu · 2022
Closest in time.
Interpretable and generalizable graph learning via stochastic attention mechanism
Siqi Miao, Miaoyuan Liu, and Pan Li · 2022
Closest in time.
Ood-gnn: Out-of-distribution generalized graph neural network
Haoyang Li, Xin Wang, Ziwei Zhang, and Wenwu Zhu · 2022
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, and Aaron Courville · 2021
Cited alongside, same era.
Generalizing graph neural networks on out-of-distribution graphs
Shaohua Fan, Xiao Wang, Chuan Shi, Peng Cui, and Bai Wang · 2021
Cited alongside, same era.
Tradeoffs in data augmentation: An empirical study
Raphael Gontijo-Lopes, Sylvia Smullin, Ekin Dogus Cubuk, and Ethan Dyer · 2021
Cited alongside, same era.
A closer look at distribution shifts and out-of-distribution generalization on graphs
Mucong Ding, Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Micah Goldblum, David Wipf, Furong Huang, and Tom Goldstein · 2021
Cited alongside, same era.
Invariant causal representation learning for out-of-distribution generalization
Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, and Bernhard Schölkopf · 2021
Cited alongside, same era.
Shift-robust gnns: Overcoming the limitations of localized graph training data
Qi Zhu, Natalia Ponomareva, Jiawei Han, and Bryan Perozzi · 2021
Cited alongside, same era.
Heterogeneous risk minimization
Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, and Zheyan Shen · 2021
Cited alongside, same era.
Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, and James Cheng · 2022
Closest in time.
Model-agnostic augmentation for accurate graph classification
Jaemin Yoo, Sooyeon Shim, and U Kang · 2022
Closest in time.
Graphde: A generative framework for debiased learning and out-of-distribution detection on graphs
Zenan Li, Qitian Wu, Fan Nie, and Junchi Yan · 2022
Closest in time.
A theoretical analysis on independence-driven importance weighting for covariate-shift generalization
Renzhe Xu, Xingxuan Zhang, Zheyan Shen, Tong Zhang, and Peng Cui · 2022
Closest in time.
Toward learning robust and invariant representations with alignment regularization and data augmentation
Haohan Wang, Zeyi Huang, Xindi Wu, and Eric Xing · 2022
Closest in time.
Generalizing to unseen domains: A survey on domain generalization
Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, and Philip Yu · 2022
Closest in time.
Sizeshiftreg: a regularization method for improving size-generalization in graph neural networks
Davide Buffelli, Pietro Lio, and Fabio Vandin · 2022
Closest in time.
Dynamic graph neural networks under spatio-temporal distribution shift
Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Zhou Qin, and Wenwu Zhu · 2022
Closest in time.
On regularization for explaining graph neural networks: An information theory perspective
Junfeng Fang, Wei Liu, An Zhang, Xiang Wang, Xiangnan He, Kun Wang, and Tat-Seng Chua · 2022
Closest in time.
Exploring lottery ticket hypothesis in media recommender systems
Yanfang Wang, Yongduo Sui, Xiang Wang, Zhenguang Liu, and Xiangnan He · 2022
Closest in time.
A survey on deep graph generation: Methods and applications
Yanqiao Zhu, Yuanqi Du, Yinkai Wang, Yichen Xu, Jieyu Zhang, Qiang Liu, and Shu Wu · 2022
Closest in time.
Graph neural networks are inherently good generalizers: Insights by bridging GNNs and MLPs
Chenxiao Yang, Qitian Wu, Jiahua Wang, and Junchi Yan · 2023
Closest in time.
Invariant collaborative filtering to popularity distribution shift
An Zhang, Jingnan Zheng, Xiang Wang, Yancheng Yuan, and Tat-Seng Chua · 2023
Closest in time.
Alleviating structural distribution shift in graph anomaly detection
Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, and Yongdong Zhang · 2023
Closest in time.
Empowering graph representation learning with test-time graph transformation
Wei Jin, Tong Zhao, Jiayuan Ding, Yozen Liu, Jiliang Tang, and Neil Shah · 2023
Closest in time.
Mind the label shift of augmentation-based graph ood generalization
Junchi Yu, Jian Liang, and Ran He · 2023
Closest in time.
Energy-based out-of-distribution detection for graph neural networks
Qitian Wu, Yiting Chen, Chenxiao Yang, and Junchi Yan · 2023
Closest in time.
A data-centric framework to endow graph neural networks with out-of-distribution detection ability
Yuxin Guo, Cheng Yang, Yuluo Chen, Jixi Liu, Chuan Shi, and Junping Du · 2023
Closest in time.
Covariate-shift generalization via random sample weighting
Yue He, Xinwei Shen, Renzhe Xu, Tong Zhang, Yong Jiang, Wenchao Zou, and Peng Cui · 2023
Closest in time.
Graph domain adaptation via theory-grounded spectral regularization
Yuning You, Tianlong Chen, Zhangyang Wang, and Yang Shen · 2023
Closest in time.
Cooperative explanations of graph neural networks
Junfeng Fang, Xiang Wang, An Zhang, Zemin Liu, Xiangnan He, and Tat-Seng Chua · 2023
Closest in time.
Inductive lottery ticket learning for graph neural networks
Yongduo Sui, Xiang Wang, Tianlong Chen, Meng Wang, Xiangnan He, and Tat-Seng Chua · 2023
Closest in time.
Addressing heterophily in graph anomaly detection: A perspective of graph spectrum
Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, and Yongdong Zhang · 2023
Closest in time.
Rumor detection with self-supervised learning on texts and social graph
Yuan Gao, Xiang Wang, Xiangnan He, Huamin Feng, and Yong-Dong Zhang · 2023
Closest in time.