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Interpretable and Generalizable Graph Learning via Stochastic Attention
  Mechanism
v1v2v3 (latest)

Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism

International Conference on Machine Learning (ICML), 2022
31 January 2022
Siqi Miao
Miaoyuan Liu
Pan Li
ArXiv (abs)PDFHTMLGithub (167★)

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

50 / 82 papers shown
ARM-Explainer -- Explaining and improving graph neural network predictions for the maximum clique problem using node features and association rule mining
ARM-Explainer -- Explaining and improving graph neural network predictions for the maximum clique problem using node features and association rule mining
Bharat Sharman
Elkafi Hassini
161
0
0
28 Nov 2025
PISA: Prioritized Invariant Subgraph Aggregation
PISA: Prioritized Invariant Subgraph Aggregation
Ali Ghasemi
F. Wani
Maria Sofia Bucarelli
Fabrizio Silvestri
OOD
135
0
0
27 Nov 2025
Evolving Graph Learning for Out-of-Distribution Generalization in Non-stationary Environments
Evolving Graph Learning for Out-of-Distribution Generalization in Non-stationary EnvironmentsIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2025
Qingyun Sun
Jiayi Luo
Haonan Yuan
Xingcheng Fu
Hao Peng
Jianxin Li
Philip S. Yu
210
0
0
04 Nov 2025
Leveraging Classical Algorithms for Graph Neural Networks
Leveraging Classical Algorithms for Graph Neural Networks
Jason Wu
Petar Veličković
GNNAI4CE
211
0
0
24 Oct 2025
Quantifying Distributional Invariance in Causal Subgraph for IRM-Free Graph Generalization
Quantifying Distributional Invariance in Causal Subgraph for IRM-Free Graph Generalization
Yang Qiu
Yixiong Zou
Jun Wang
Wei Liu
Xiangyu Fu
R. Li
OOD
183
1
0
23 Oct 2025
TopInG: Topologically Interpretable Graph Learning via Persistent Rationale Filtration
TopInG: Topologically Interpretable Graph Learning via Persistent Rationale Filtration
Cheng Xin
Fan Xu
Xin Ding
Jie Gao
Jiaxin Ding
171
0
0
06 Oct 2025
SoREX: Towards Self-Explainable Social Recommendation with Relevant Ego-Path Extraction
SoREX: Towards Self-Explainable Social Recommendation with Relevant Ego-Path Extraction
Hanze Guo
Yijun Ma
Xiao Zhou
196
2
0
30 Sep 2025
Two Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural Process
Two Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural ProcessInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2025
Lingkai Kong
Haotian Sun
Yuchen Zhuang
Haorui Wang
Wenhao Mu
Chao Zhang
BDL
170
5
0
23 Aug 2025
Towards Faithful Class-level Self-explainability in Graph Neural Networks by Subgraph Dependencies
Towards Faithful Class-level Self-explainability in Graph Neural Networks by Subgraph Dependencies
Fanzhen Liu
Xiaoxiao Ma
Jian Yang
A. Abuadbba
Kristen Moore
Surya Nepal
Cécile Paris
Quan Z. Sheng
Jia Wu
141
1
0
15 Aug 2025
Enhancement of Quantum Semi-Supervised Learning via Improved Laplacian and Poisson Methods
Enhancement of Quantum Semi-Supervised Learning via Improved Laplacian and Poisson Methods
Hamed Gholipour
Farid Bozorgnia
Hamzeh Mohammadigheymasi
Kailash A. Hambarde
Javier Mancilla
Hugo Proença
Joao Neves
Moharram Challenger
93
0
0
04 Aug 2025
Invariant Graph Transformer for Out-of-Distribution Generalization
Invariant Graph Transformer for Out-of-Distribution Generalization
Tianyin Liao
Ziwei Zhang
Yufei Sun
Chunyu Hu
Jianxin Li
OOD
179
1
0
01 Aug 2025
Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks
Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks
Jiaxing Zhang
Xiaoou Liu
Dongsheng Luo
Hua Wei
FAtt
225
2
0
31 May 2025
Invariant Link Selector for Spatial-Temporal Out-of-Distribution Problem
Invariant Link Selector for Spatial-Temporal Out-of-Distribution ProblemInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2025
Katherine Tieu
Dongqi Fu
Jun Wu
Jingrui He
OODOODDCML
198
5
0
30 May 2025
MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration
MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration
Rishabh Bhattacharya
Hari Shankar
Vaishnavi Shivkumar
Ponnurangam Kumaraguru
209
0
0
26 May 2025
Learning Repetition-Invariant Representations for Polymer Informatics
Learning Repetition-Invariant Representations for Polymer Informatics
Yihan Zhu
Gang Liu
Eric Inae
Tengfei Luo
Meng Jiang
356
0
0
15 May 2025
From GNNs to Trees: Multi-Granular Interpretability for Graph Neural Networks
From GNNs to Trees: Multi-Granular Interpretability for Graph Neural NetworksInternational Conference on Learning Representations (ICLR), 2025
Jie Yang
Yuwen Wang
Kaixuan Chen
Tongya Zheng
Yihe Zhou
Zhenbang Xiao
Ji Cao
Mingli Song
Shixuan Liu
AI4CE
307
3
0
01 May 2025
Enhancing Job Salary Prediction with Disentangled Composition Effect Modeling: A Neural Prototyping Approach
Enhancing Job Salary Prediction with Disentangled Composition Effect Modeling: A Neural Prototyping Approach
Yang Ji
Ying Sun
Hengshu Zhu
429
3
0
17 Mar 2025
Interpretable High-order Knowledge Graph Neural Network for Predicting Synthetic Lethality in Human Cancers
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
337
1
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
287
1
0
04 Mar 2025
Learning to Discover Regulatory Elements for Gene Expression Prediction
Learning to Discover Regulatory Elements for Gene Expression PredictionInternational Conference on Learning Representations (ICLR), 2025
Xingyu Su
Haiyang Yu
D. Zhi
Shuiwang Ji
265
4
0
19 Feb 2025
Recent Advances in Malware Detection: Graph Learning and Explainability
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
556
13
0
14 Feb 2025
BrainOOD: Out-of-distribution Generalizable Brain Network Analysis
BrainOOD: Out-of-distribution Generalizable Brain Network AnalysisInternational Conference on Learning Representations (ICLR), 2025
Jiaxing Xu
Yongqiang Chen
Xia Dong
Mengcheng Lan
Tiancheng Huang
Qingtian Bian
James Cheng
Yiping Ke
OOD
426
6
0
02 Feb 2025
Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization
Enhancing Distribution and Label Consistency for Graph Out-of-Distribution GeneralizationIndustrial Conference on Data Mining (IDM), 2024
Song Wang
Xiaodong Yang
Rashidul Islam
Huiyuan Chen
Minghua Xu
Jundong Li
Yiwei Cai
OODD
438
4
0
07 Jan 2025
Subgraph Aggregation for Out-of-Distribution Generalization on Graphs
Subgraph Aggregation for Out-of-Distribution Generalization on GraphsAAAI Conference on Artificial Intelligence (AAAI), 2024
Bowen Liu
Haoyang Li
Shuning Wang
Shuo Nie
Shanghang Zhang
OODDCML
286
2
0
29 Oct 2024
A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation
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
423
12
0
25 Oct 2024
DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization
DIVE: Subgraph Disagreement for Graph Out-of-Distribution GeneralizationKnowledge Discovery and Data Mining (KDD), 2024
Xin Sun
Liang Wang
Qiang Liu
Shu Wu
Zilei Wang
Liang Wang
OODCML
298
11
0
08 Aug 2024
Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution Generalization
Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution Generalization
Wenyu Mao
Jiancan Wu
Haoyang Liu
Yongduo Sui
Xiang Wang
OOD
452
6
0
03 Aug 2024
xAI-Drop: Don't Use What You Cannot Explain
xAI-Drop: Don't Use What You Cannot ExplainLOG IN (LOG IN), 2024
Vincenzo Marco De Luca
Antonio Longa
Baptiste Caramiaux
Pietro Lio
339
1
0
29 Jul 2024
Explaining Graph Neural Networks for Node Similarity on Graphs
Explaining Graph Neural Networks for Node Similarity on Graphs
Daniel Daza
C. Chu
T. Tran
Daria Stepanova
Michael Cochez
Paul T. Groth
208
3
0
10 Jul 2024
Unveiling Global Interactive Patterns across Graphs: Towards
  Interpretable Graph Neural Networks
Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks
Yuwen Wang
Shunyu Liu
Tongya Zheng
Kaixuan Chen
Mingli Song
AI4CE
226
9
0
02 Jul 2024
How Interpretable Are Interpretable Graph Neural Networks?
How Interpretable Are Interpretable Graph Neural Networks?
Yongqiang Chen
Yatao Bian
Bo Han
James Cheng
218
13
0
12 Jun 2024
GNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks
GNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks
Hsiao-Ying Lu
Yiran Li
Ujwal Pratap Krishna Kaluvakolanu Thyagarajan
K. Ma
313
1
0
06 Jun 2024
Negative as Positive: Enhancing Out-of-distribution Generalization for
  Graph Contrastive Learning
Negative as Positive: Enhancing Out-of-distribution Generalization for Graph Contrastive Learning
Zixu Wang
Bingbing Xu
Yige Yuan
Huawei Shen
Xueqi Cheng
OODD
243
4
0
25 May 2024
SubGDiff: A Subgraph Diffusion Model to Improve Molecular Representation
  Learning
SubGDiff: A Subgraph Diffusion Model to Improve Molecular Representation LearningNeural Information Processing Systems (NeurIPS), 2024
Jiying Zhang
Zijing Liu
Yu Wang
Yu-Feng Li
DiffM
231
7
0
09 May 2024
Contextualized Messages Boost Graph Representations
Contextualized Messages Boost Graph Representations
Brian Godwin Lim
Galvin Brice Lim
Renzo Roel Tan
Kazushi Ikeda
AI4CE
530
5
0
19 Mar 2024
Cooperative Classification and Rationalization for Graph Generalization
Cooperative Classification and Rationalization for Graph GeneralizationThe Web Conference (WWW), 2024
Linan Yue
Qi Liu
Ye Liu
Weibo Gao
Fangzhou Yao
Wenfeng Li
245
12
0
10 Mar 2024
Unifying Invariance and Spuriousity for Graph Out-of-Distribution via
  Probability of Necessity and Sufficiency
Unifying Invariance and Spuriousity 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
215
2
0
14 Feb 2024
PowerGraph: A power grid benchmark dataset for graph neural networks
PowerGraph: A power grid benchmark dataset for graph neural networks
Anna Varbella
Kenza Amara
B. Gjorgiev
Mennatallah El-Assady
G. Sansavini
199
16
0
05 Feb 2024
Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution
  Generalization
Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution Generalization
Tianrui Jia
Haoyang Li
Cheng Yang
Tao Tao
Chuan Shi
OOD
216
31
0
18 Dec 2023
Factorized Explainer for Graph Neural Networks
Factorized Explainer for Graph Neural NetworksAAAI Conference on Artificial Intelligence (AAAI), 2023
Rundong Huang
Farhad Shirani
Dongsheng Luo
221
15
0
09 Dec 2023
GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of
  Aligned Experts
GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned ExpertsNeural Information Processing Systems (NeurIPS), 2023
Shirley Wu
Kaidi Cao
Bruno Ribeiro
James Zou
J. Leskovec
OOD
241
12
0
07 Dec 2023
Generative Explanations for Graph Neural Network: Methods and
  Evaluations
Generative Explanations for Graph Neural Network: Methods and Evaluations
Jialin Chen
Kenza Amara
Junchi Yu
Rex Ying
232
5
0
09 Nov 2023
Explainable Spatio-Temporal Graph Neural Networks
Explainable Spatio-Temporal Graph Neural NetworksInternational Conference on Information and Knowledge Management (CIKM), 2023
Jiabin Tang
Lianghao Xia
Chao Huang
AI4TS
301
30
0
26 Oct 2023
Towards Self-Interpretable Graph-Level Anomaly Detection
Towards Self-Interpretable Graph-Level Anomaly DetectionNeural Information Processing Systems (NeurIPS), 2023
Yixin Liu
Kaize Ding
Qinghua Lu
Fuyi Li
Leo Yu Zhang
Shirui Pan
280
80
0
25 Oct 2023
Learning Invariant Molecular Representation in Latent Discrete Space
Learning Invariant Molecular Representation in Latent Discrete SpaceNeural Information Processing Systems (NeurIPS), 2023
Zhuang Xiang
Qiang Zhang
Keyan Ding
Yatao Bian
Xiao Wang
Jingsong Lv
Hongyang Chen
Huajun Chen
OOD
231
28
0
22 Oct 2023
Graph AI in Medicine
Graph AI in Medicine
Ruth Johnson
Michelle M. Li
Ayush Noori
Owen Queen
Marinka Zitnik
347
4
0
20 Oct 2023
GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers
  through In-depth Benchmarking
GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth BenchmarkingInternational Conference on Learning Representations (ICLR), 2023
Mert Kosan
S. Verma
Burouj Armgaan
Khushbu Pahwa
Ambuj K. Singh
Sourav Medya
Jignesh M. Patel
382
19
0
03 Oct 2023
GInX-Eval: Towards In-Distribution Evaluation of Graph Neural Network
  Explanations
GInX-Eval: Towards In-Distribution Evaluation of Graph Neural Network Explanations
Kenza Amara
Mennatallah El-Assady
Rex Ying
164
7
0
28 Sep 2023
Temporal Inductive Path Neural Network for Temporal Knowledge Graph
  Reasoning
Temporal Inductive Path Neural Network for Temporal Knowledge Graph ReasoningArtificial Intelligence (AIJ), 2023
Hao Dong
Pengyang Wang
Meng Xiao
Zhiyuan Ning
P. Wang
Yuanchun Zhou
344
43
0
06 Sep 2023
How Faithful are Self-Explainable GNNs?
How Faithful are Self-Explainable GNNs?
Marc Christiansen
Lea Villadsen
Zhiqiang Zhong
Stefano Teso
Davide Mottin
186
4
0
29 Aug 2023
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