Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism

31 January 2022

Papers citing "Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism"

32 / 32 papers shown

Title
Soft causal learning for generalized molecule property prediction: An environment perspective Limin Li Kuo Yang Wenjie Du Pengkun Wang Zhengyang Zhou Yang Wang OOD AI4CE 48 0 0 07 May 2025
Enhancing Job Salary Prediction with Disentangled Composition Effect Modeling: A Neural Prototyping Approach Yang Ji Ying Sun Hengshu Zhu 46 0 0 17 Mar 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 41 0 0 08 Mar 2025
NodeNAS: Node-Specific Graph Neural Architecture Search for Out-of-Distribution Generalization Qiyi Wang Yinning Shao Yunlong Ma Min Liu OOD 71 0 0 04 Mar 2025
Recent Advances in Malware Detection: Graph Learning and Explainability Hossein Shokouhinejad Roozbeh Razavi-Far Hesamodin Mohammadian Mahdi Rabbani Samuel Ansong Griffin Higgins Ali Ghorbani AAML 68 2 0 14 Feb 2025
Subgraph Aggregation for Out-of-Distribution Generalization on Graphs Bowen Liu Haoyang Li Shuning Wang Shuo Nie Shanghang Zhang OODD CML 58 0 0 29 Oct 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 R. Li Jundong Li Kaize Ding OOD 45 2 0 25 Oct 2024
Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution Generalization Wenyu Mao Jiancan Wu Haoyang Liu Yongduo Sui Xiang Wang OOD 37 2 0 03 Aug 2024
Unifying Invariant and Variant Features for Graph Out-of-Distribution via Probability of Necessity and Sufficiency Xuexin Chen Ruichu Cai Kaitao Zheng Zhifan Jiang Zhengting Huang Zhifeng Hao Zijian Li 44 0 0 21 Jul 2024
Contextualized Messages Boost Graph Representations Brian Godwin Lim Galvin Brice Lim Renzo Roel Tan Kazushi Ikeda AI4CE 62 1 0 19 Mar 2024
Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks Zhuomin Chen Jiaxing Zhang Jingchao Ni Xiaoting Li Yuchen Bian Md. Mezbahul Islam A. Mondal Hua Wei Dongsheng Luo 16 1 0 03 Feb 2024
Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution Generalization Tianrui Jia Haoyang Li Cheng Yang Tao Tao Chuan Shi OOD 20 17 0 18 Dec 2023
GRAM: An Interpretable Approach for Graph Anomaly Detection using Gradient Attention Maps Yifei Yang Peng Wang Xiaofan He Dongmian Zou 14 5 0 10 Nov 2023
Identifying Semantic Component for Robust Molecular Property Prediction Zijian Li Zunhong Xu Ruichu Cai Zhenhui Yang Yuguang Yan Zhifeng Hao Guan-Hong Chen Kun Zhang 18 9 0 08 Nov 2023
Does Invariant Graph Learning via Environment Augmentation Learn Invariance? Yongqiang Chen Yatao Bian Kaiwen Zhou Binghui Xie Bo Han James Cheng OOD 26 33 0 29 Oct 2023
Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization Ling Yang Jiayi Zheng Heyuan Wang Zhongyi Liu Zhilin Huang Shenda Hong Wentao Zhang Bin Cui 22 13 0 28 Jun 2023
Robust Ante-hoc Graph Explainer using Bilevel Optimization Kha-Dinh Luong Mert Kosan A. Silva Ambuj K. Singh 30 6 0 25 May 2023
Unstructured and structured data: Can we have the best of both worlds with large language models? W. Tan 11 1 0 25 Apr 2023
Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability Indro Spinelli Michele Guerra F. Bianchi Simone Scardapane 25 0 0 14 Apr 2023
Quantum Graph Learning: Frontiers and Outlook Shuo Yu Ciyuan Peng Yingbo Wang Ahsan Shehzad Feng Xia Edwin R. Hancock AI4CE 16 5 0 02 Feb 2023
CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis Kaizhong Zheng Shujian Yu Badong Chen CML 21 31 0 04 Jan 2023
Towards Prototype-Based Self-Explainable Graph Neural Network Enyan Dai Suhang Wang 25 12 0 05 Oct 2022
L2XGNN: Learning to Explain Graph Neural Networks G. Serra Mathias Niepert 26 7 0 28 Sep 2022
Trustworthy Graph Neural Networks: Aspects, Methods and Trends He Zhang Bang Wu Xingliang Yuan Shirui Pan Hanghang Tong Jian Pei 41 104 0 16 May 2022
Graph Data Augmentation for Graph Machine Learning: A Survey Tong Zhao Wei Jin Yozen Liu Yingheng Wang Gang Liu Stephan Günnemann Neil Shah Meng-Long Jiang OOD 25 79 0 17 Feb 2022
Task-Agnostic Graph Explanations Yaochen Xie S. Katariya Xianfeng Tang E-Wen Huang Nikhil S. Rao Karthik Subbian Shuiwang Ji 30 25 0 16 Feb 2022
Discovering Invariant Rationales for Graph Neural Networks Yingmin Wu Xiang Wang An Zhang Xiangnan He Tat-Seng Chua OOD AI4CE 89 222 0 30 Jan 2022
Explainability in Graph Neural Networks: A Taxonomic Survey Hao Yuan Haiyang Yu Shurui Gui Shuiwang Ji 162 590 0 31 Dec 2020
Graph Information Bottleneck Tailin Wu Hongyu Ren Pan Li J. Leskovec AAML 88 224 0 24 Oct 2020
Invariant Rationalization Shiyu Chang Yang Zhang Mo Yu Tommi Jaakkola 179 201 0 22 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 215 900 0 02 Mar 2020
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 162 1,775 0 02 Mar 2017