Discovering Invariant Rationales for Graph Neural Networks

30 January 2022

Xiang Wang

Papers citing "Discovering Invariant Rationales for Graph Neural Networks"

49 / 49 papers shown

Title
Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks Han Zhang Yan Wang Guanfeng Liu Pengfei Ding Huaxiong Wang Kwok-Yan Lam 24 0 0 20 May 2025
Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets Wen Liu Zhongyu Niu Lang Gao Zhiying Deng Jun Wang Haobo Wang Ruixuan Li 333 1 0 04 May 2025
Interpretable High-order Knowledge Graph Neural Network for Predicting Synthetic Lethality in Human Cancers Xuexin Chen Ruichu Cai Zhengting Huang Zijian Li Jie Zheng Min Wu 57 0 0 08 Mar 2025
DivIL: Unveiling and Addressing Over-Invariance for Out-of- Distribution Generalization Jiaqi Wang Yuhang Zhou Zhixiong Zhang Qiguang Chen Yongqiang Chen James Cheng OODD 101 1 0 18 Feb 2025
Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Song Wang Xiaodong Yang Rashidul Islam Huiyuan Chen Minghua Xu Jundong Li Yiwei Cai OODD 130 2 0 07 Jan 2025
SSL Framework for Causal Inconsistency between Structures and Representations Hang Chen Xinyu Yang Keqing Du Wenya Wang 66 2 0 03 Jan 2025
Causal-aware Graph Neural Architecture Search under Distribution Shifts Peiwen Li Xin Wang Zeyang Zhang Yi Qin Ziwei Zhang Jialong Wang Yang Li Wenwu Zhu CML OOD 80 4 0 31 Dec 2024
Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination Md Abrar Jahin Md. Akmol Masud M. F. Mridha Nilanjan Dey Zeyar Aung 70 0 0 03 Nov 2024
A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation Kexin Zhang Shuhan Liu Song Wang Weili Shi Chen Chen Pan Li Sheng Li Jundong Li Kaize Ding OOD 81 3 0 25 Oct 2024
Leveraging Invariant Principle for Heterophilic Graph Structure Distribution Shifts Jinluan Yang Zhengyu Chen Teng Xiao Wenqiao Zhang Yong Lin Kun Kuang 67 2 0 18 Aug 2024
Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution Generalization Wenyu Mao Jiancan Wu Haoyang Liu Yongduo Sui Xiang Wang OOD 64 2 0 03 Aug 2024
Self-Supervised Learning Disentangled Group Representation as Feature Tan Wang Zhongqi Yue Jianqiang Huang Qianru Sun Hanwang Zhang OOD 49 67 0 28 Oct 2021
Causal Attention for Unbiased Visual Recognition Tan Wang Chan Zhou Qianru Sun Hanwang Zhang OOD CML 59 111 0 19 Aug 2021
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs Weihua Hu Matthias Fey Hongyu Ren Maho Nakata Yuxiao Dong J. Leskovec AI4CE 39 403 0 17 Mar 2021
Size-Invariant Graph Representations for Graph Classification Extrapolations Beatrice Bevilacqua Yangze Zhou Bruno Ribeiro OOD 67 110 0 08 Mar 2021
On Explainability of Graph Neural Networks via Subgraph Explorations Hao Yuan Haiyang Yu Jie Wang Kang Li Shuiwang Ji FAtt 44 383 0 09 Feb 2021
Does Invariant Risk Minimization Capture Invariance? Pritish Kamath Akilesh Tangella Danica J. Sutherland Nathan Srebro OOD 240 127 0 04 Jan 2021
Parameterized Explainer for Graph Neural Network Dongsheng Luo Wei Cheng Dongkuan Xu Wenchao Yu Bo Zong Haifeng Chen Xiang Zhang 108 546 0 09 Nov 2020
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective Kartik Ahuja Jun Wang Amit Dhurandhar Karthikeyan Shanmugam Kush R. Varshney OOD 48 79 0 30 Oct 2020
Environment Inference for Invariant Learning Elliot Creager J. Jacobsen R. Zemel OOD 33 376 0 14 Oct 2020
The Risks of Invariant Risk Minimization Elan Rosenfeld Pradeep Ravikumar Andrej Risteski OOD 35 307 0 12 Oct 2020
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting Giorgos Bouritsas Fabrizio Frasca Stefanos Zafeiriou M. Bronstein 78 428 0 16 Jun 2020
Risk Variance Penalization Chuanlong Xie Haotian Ye Fei Chen Yue Liu Rui Sun Zhenguo Li 83 33 0 13 Jun 2020
Open Graph Benchmark: Datasets for Machine Learning on Graphs Weihua Hu Matthias Fey Marinka Zitnik Yuxiao Dong Hongyu Ren Bowen Liu Michele Catasta J. Leskovec 145 2,687 0 02 May 2020
Invariant Rationalization Shiyu Chang Yang Zhang Mo Yu Tommi Jaakkola 224 204 0 22 Mar 2020
Benchmarking Graph Neural Networks Vijay Prakash Dwivedi Chaitanya K. Joshi Anh Tuan Luu T. Laurent Yoshua Bengio Xavier Bresson 235 929 0 02 Mar 2020
Out-of-Distribution Generalization via Risk Extrapolation (REx) David M. Krueger Ethan Caballero J. Jacobsen Amy Zhang Jonathan Binas Dinghuai Zhang Rémi Le Priol Aaron Courville OOD 277 921 0 02 Mar 2020
Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization Shiori Sagawa Pang Wei Koh Tatsunori B. Hashimoto Percy Liang OOD 33 1,217 0 20 Nov 2019
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations Ekagra Ranjan Soumya Sanyal Partha P. Talukdar GNN 56 330 0 18 Nov 2019
Invariant Risk Minimization Martín Arjovsky Léon Bottou Ishaan Gulrajani David Lopez-Paz OOD 135 2,190 0 05 Jul 2019
RUBi: Reducing Unimodal Biases in Visual Question Answering Rémi Cadène Corentin Dancette H. Ben-younes Matthieu Cord Devi Parikh CML 55 373 0 24 Jun 2019
On the equivalence between graph isomorphism testing and function approximation with GNNs Zhengdao Chen Soledad Villar Lei Chen Joan Bruna 48 278 0 29 May 2019
Provably Powerful Graph Networks Haggai Maron Heli Ben-Hamu Hadar Serviansky Y. Lipman 46 575 0 27 May 2019
Graph U-Nets Hongyang Gao Shuiwang Ji AI4CE SSL SSeg GNN 52 1,073 0 11 May 2019
Understanding Attention and Generalization in Graph Neural Networks Boris Knyazev Graham W. Taylor Mohamed R. Amer GNN 47 338 0 08 May 2019
Self-Attention Graph Pooling Junhyun Lee Inyeop Lee Jaewoo Kang GNN 69 1,110 0 17 Apr 2019
GNNExplainer: Generating Explanations for Graph Neural Networks Rex Ying Dylan Bourgeois Jiaxuan You Marinka Zitnik J. Leskovec LLMAG 94 1,300 0 10 Mar 2019
Graph Neural Networks with convolutional ARMA filters F. Bianchi Daniele Grattarola L. Livi Cesare Alippi GNN 42 389 0 05 Jan 2019
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Jacob Devlin Ming-Wei Chang Kenton Lee Kristina Toutanova VLM SSL SSeg 681 93,936 0 11 Oct 2018
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks Christopher Morris Martin Ritzert Matthias Fey William L. Hamilton J. E. Lenssen Gaurav Rattan Martin Grohe GNN 114 1,625 0 04 Oct 2018
How Powerful are Graph Neural Networks? Keyulu Xu Weihua Hu J. Leskovec Stefanie Jegelka GNN 111 7,554 0 01 Oct 2018
Graph Attention Networks Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio Yoshua Bengio GNN 237 19,902 0 30 Oct 2017
A causal framework for explaining the predictions of black-box sequence-to-sequence models David Alvarez-Melis Tommi Jaakkola CML 291 204 0 06 Jul 2017
Attention Is All You Need Ashish Vaswani Noam M. Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan Gomez Lukasz Kaiser Illia Polosukhin 3DV 268 129,831 0 12 Jun 2017
Inductive Representation Learning on Large Graphs William L. Hamilton Z. Ying J. Leskovec 291 15,066 0 07 Jun 2017
MoleculeNet: A Benchmark for Molecular Machine Learning Zhenqin Wu Bharath Ramsundar Evan N. Feinberg Joseph Gomes C. Geniesse Aneesh S. Pappu K. Leswing Vijay S. Pande OOD 283 1,808 0 02 Mar 2017
Semi-Supervised Classification with Graph Convolutional Networks Thomas Kipf Max Welling GNN SSL 346 28,795 0 09 Sep 2016
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 354 149,474 0 22 Dec 2014
Representation Learning: A Review and New Perspectives Yoshua Bengio Aaron Courville Pascal Vincent OOD SSL 114 12,384 0 24 Jun 2012