GNN Explainability

GNN: Graph Neural Network

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3 papers in the last four weeks, down 25% on the four weeks before. 0.0% of all new papers.

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ST-TGExplainer: Disentangling Stability and Transition Patterns for Temporal GNN Interpretability
May 19, 2026Hongjiang Chen, Xin Zheng, Pengfei Jiao +5Disentangled Representation Learning Temporal GNNs
Hangzhou Dianzi University, Hangzhou, China · RMIT University, Melbourne, Australia · Tianjin University, Tianjin, China +1
B-cos GNNs: Faithful Explanations through Dynamic Linearity
May 19, 2026Joschka Groß, Mohammad Shaique Solanki, Verena WolfFeature Attribution Graph Neural Networks
Saarland Informatics Campus, Saarland University · DFKI
UNR-Explainer: Counterfactual Explanations for Unsupervised Node Representation Learning Models
May 17, 2026Hyunju Kang, Geonhee Han, Hogun ParkCounterfactual Explanations Unsupervised Representation Learning
Department of Artificial Intelligence Sungkyunkwan University Suwon, Republic of Korea
AIMing for Standardised Explainability Evaluation in GNNs: A Framework and Case Study on Graph Kernel Networks
May 14, 2026Magdalena Proszewska, N. SiddharthGraph Neural Networks Interpretable ML
School of Informatics, University of Edinburgh
Estimating Subgraph Importance with Structural Prior Domain Knowledge
May 12, 2026Changhyun Kim, Seunghwan An, Jong-June JeonGraph Neural Networks GNN Explainability
University of Seoul, Seoul, South Korea · Incheon National University, Incheon, South Korea
Hierarchical Attention-based Graph Neural Network with Relevance-driven Pruning
May 10, 2026Seungwoo KumGraph Attention Networks Heterogeneous GNNs
Korea Electronics Technology Institute (KETI) Seongnam, Republic of Korea
Structural Interpretations of Protein Language Model Representations via Differentiable Graph Partitioning
May 9, 2026Siddhant Dutta, Edward Tan Beng Wai, Soumick Sarker +2Graph Neural Networks Protein Language Models
Nanyang Technological University, Singapore
Graph neural network explanations reveal a topological signature of disease-associated hubs in biological networks
May 8, 2026Kyle Higgins, Ivan Laponogov, Dennis Veselkov +1Cancer Genomics Feature Attribution
1Division of Cancer, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK. · Department of Computing, Imperial College London, U.K. · Department of Environmental Health Sciences, Yale University, New Haven, CT, USA.
Why Self-Inconsistency Arises in GNN Explanations and How to Exploit It
May 8, 2026Wenxin Tai, Yaqian Liu, Ting Zhong +1Explanation Stability GNN Explainability
University of Electronic Science and Technology of China
Graph Reconstruction from Differentially Private GNN Explanations
May 5, 2026Rishi Raj Sahoo, Jyotirmaya Shivottam, Subhankar MishraData Leakage Differential Privacy
National Institute of Science Education and Research (NISER), Bhubaneswar, India · Homi Bhabha National Institute, Mumbai, India
GRAFT: Auditing Graph Neural Networks via Global Feature Attribution
May 5, 2026Rishi Raj Sahoo, Subhankar MishraFeature Attribution Graph Neural Networks
National Institute of Science Education and Research (NISER), Bhubaneswar, India · Homi Bhabha National Institute, Mumbai, India
Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane
Apr 28, 2026Pahal D. Patel, Sanmay GangulyGradient-Based Attribution Jet Tagging
Department of Physics, Indian Institute of Technology, Kanpur Uttar-Pradesh-208016, India
Explaining Temporal Graph Predictions With Shapley Values
Apr 27, 2026Lea-Marie Sussek, Stefan HeindorfTemporal GNNs Shapley Value Attribution
Paderborn University Paderborn, Germany
Operational Feature Fingerprints of Graph Datasets via a White-Box Signal-Subspace Probe
Apr 24, 2026Yuchen Xiong, Swee Keong Yeap, Zhen Hong BanGraph Signal Processing Node Classification
China-ASEAN College of Marine Sciences · Xiamen University Malaysia · Sepang 43900, Selangor, Malaysia +1
Mechanistic Interpretability Tool for AI Weather Models
Apr 22, 2026Kirsten I. Tempest, Matthias Beylich, George C. CraigInterpretable ML Mechanistic Interpretability
Meteorological Institute Munich, Ludwig-Maximilians-Universität, 80333 Munich, Germany · Deutsches Zentrum für Luft- und Raumfahrt, Oberpfaffenhofen, Germany
Concept Graph Convolutions: Message Passing in the Concept Space
Apr 22, 2026Lucie Charlotte Magister, Pietro LioGraph Neural Networks Message Passing Neural Networks
University of Cambridge
TACENR: Task-Agnostic Contrastive Explanations for Node Representations
Apr 21, 2026Vasiliki Papanikou, Evaggelia PitouraGraph Representation Learning GNN Explainability
University of Ioannina, Greece · Archimedes/Athena Research Center, Greece
Subgraph Concept Networks: Concept Levels in Graph Classification
Apr 20, 2026Lucie Charlotte Magister, Alexander Norcliffe, Iulia Duta +1Graph Neural Networks Hierarchical Representation Learning
University of Cambridge Cambridge, UK
KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging
Dec 8, 2025Md Raqibul Islam, Adrita Khan, Mir Sazzat Hossain +6Graph Representation Learning Jet Tagging
Center for Computational & Data Sciences, Independent University, Bangladesh, Dhaka-1229, Bangladesh · Department of Theoretical Physics, University of Dhaka, Dhaka-1000, Bangladesh · Department of Physical Sciences, Independent University, Bangladesh, Dhaka-1229, Bangladesh
Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization
Jul 4, 2025Zanyu Shi, Yang Wang, Pathum M. Weerawarna +4Protein-Ligand Binding Affinity Prediction Molecular Property Prediction
Department of Biostatistics & Health Data Science, Indiana University Fairbanks School of Public Health, Indianapolis, 46202 IN, USA. · Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, 47405 IN, USA. · Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, 46202 IN, USA. +2

May 19, 2026, cs.LGJ/K move · Enter open · S save

ST-TGExplainer: Disentangling Stability and Transition Patterns for Temporal GNN Interpretability

Hongjiang Chen, Xin Zheng, Pengfei Jiao, Huan Liu+4

Hangzhou Dianzi University, Hangzhou, China · RMIT University, Melbourne, Australia · Tianjin University, Tianjin, China+1

Disentangled Representation Learning Temporal GNNs GNN Explainability

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Temporal graph neural networks (TGNNs) have gained significant traction for solving real-world temporal graph tasks. However, their interpretability remains limited, as most TGNNs fail to identify which historical interactions most influence a given prediction. Despite promising progress on interpretable TGNNs, existing methods predominantly focus on previously seen historical interactions, which we term stability patterns, while overlooking newly emerging first-time interactions, which we term transition patterns. Both types of patterns are essential for faithful temporal explanations. To address this limitation, we propose ST-TGExplainer, a self-explainable TGNN that disentangles Stability and Transition patterns in temporal graphs for a more faithful Temporal GNN Explainer. Guided by a disentangled information bottleneck objective, ST-TGExplainer learns a compact explanatory subgraph that remains predictive of the event label while explicitly suppressing label-conditioned redundancy between stability and transition patterns. Extensive experiments demonstrate that ST-TGExplainer achieves strong predictive performance and yields more faithful explanations. Code is available at https://github.com/hjchen-hdu/ST-TGExplainer.

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