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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  1. MPGE: A Multi-Perspective Graph Explainer for Molecular Classification Explanation

    Oct 8, 2026Mahtab SarvmailiGNN ExplainabilityGraph Classification

    Dalhousie University Halifax, Nova Scotia, Canada

  2. WOMBAT: Whitebox Oracle for Molecular Benchmarking and Attribution Testing

    Sep 30, 2026Dominik Matuszek, Bartosz Zieliński, Tomasz Danel +1Gradient-Based AttributionExplainability Evaluation

    Jagiellonian University, Faculty of Mathematics and Computer Science · Jagiellonian University, Doctoral School of Exact and Natural Sciences · Jagiellonian University, Jagiellonian Center for Artificial Intelligence +2

  3. HARMONIA: Interpretable Graph Learning through Mixtures of Neural Bases

    Sep 27, 2026Quan D. Bui, Nguyen Do, An Nguyen Dang +3Graph Representation LearningGNN Explainability

    Center for AI Research, VinUniversity, Hanoi, Vietnam · University of Florida, Gainesville, USA · Posts and Telecommunications Institute of Technology, Hanoi, Vietnam +1

  4. Temporal Generalization and Explanation Stability of Control Flow Graph Neural Networks for Malware Detection

    Sep 21, 2026Md. Asif Sajeed, Md. Nazrul Islam Mondal, Md Ashraful Hossen AkashGraph Neural NetworksDistribution Shift

    Department of Computer Science and Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh

  5. MolLedger: An Additive Graph Neural Network with Chemically Grounded ADME Attributions

    Aug 31, 2026Christina X. JiFeature AttributionGraph Neural Networks

    Hamilton College

  6. Dual Spatial-Temporal Attribution: Architecture-Aligned Post-Hoc Explainability for Recurrent Graph Anomaly Detection

    Aug 12, 2026Iyad Assaad Nekka, Hamida Seba, Khaled Walid Hidouci +1Gradient-Based AttributionInterpretable Anomaly Detection

    LCSI Laboratory, National Higher School of Computer Science (ESI), Algiers, Algeria · Université Claude Bernard Lyon 1, Lyon, France

  7. Faithful, Sufficient and Understandable: Rethinking Graph Counterfactual Explanations via Discrete Diffusion Inversion

    Aug 12, 2026David Bechtoldt, Sidney BenderCounterfactual EvaluationCounterfactual Explanations

    Machine Learning Group, TU Berlin, 10587 Berlin, Germany · BIFOLD–Berlin Institute for the Foundations of Learning and Data, Berlin, Germany

  8. An Explainable GNN Framework for Component-Level Anomaly Diagnosis

    Aug 10, 2026Sena Ozgunay, Louise Trav{é}-Massuy{è}s, Jean-Michel Loubes +1Industrial Anomaly DetectionRoot Cause Analysis

    Comue de Toulouse, LAAS-DISCO, IMT · LAAS-CNRS, Université de Toulouse, France · Comue de Toulouse, LAAS-DISCO +4

  9. Patients-like-me: A Variational LM--GNN Framework for Explainable Clinical Prediction

    Aug 4, 2026Xinyu Wang, Yixuan Li, Hanwei Wu +4Graph Neural NetworksClinical Outcome Prediction

    1McGill University · 2Université de Montréal · 3Georgia State University +1

  10. Automorphism-Induced Non-Canonicity in Top-k Explanations of Graph Neural Networks

    Jul 28, 2026Xin Xu, Siru Tao, Kaizhen TanGradient-Based AttributionSymmetry Breaking

    Carnegie Mellon University

  11. Learned, Relied Upon, or Necessary? Separating Checkpoint Dependence from Task-Level Value in Sheaf GNNs

    Jul 28, 2026Yi LiuSheaf Neural NetworksSheaf Theory

    School of Astronomy and Space Science, University of Science and Technology of China

  12. LatentFlow: Visual Analytics for Latent Space Analysis in Molecular Graph Neural Networks

    Jul 24, 2026Shiyi Liu, Jiaqing Chen, Nicholas Hadler +6Data VisualizationGraph Neural Networks

    Arizona State University · University of California, Berkeley · ICSI, LBNL, and University of California, Berkeley +2

  13. Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls

    Jul 23, 2026Jiancu Chen, Shuyin Xia, Guan Wang +2Graph Neural NetworksGNN Explainability

    Chongqing Key Laboratory of Computational Intelligence, Key Laboratory of Big Data Intelligent Computing, Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education, School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China · School of Computer Science and Engineering, Chongqing Three Gorges University, Chongqing, China

  14. A Polynomial Architecture-Attribution Co-Design Framework for Exact Aumann-Shapley Attribution in GNNs

    Jul 23, 2026Bizu Feng, Zhimu Yang, Shuming Wang +4Gradient-Based AttributionPolynomial Neural Networks

    Institute of Artificial Intelligence Innovation and Industry, Fudan University, Shanghai, China · Shanghai Academy of AI for Science, Shanghai, China · Human Phenome Institute, Fudan University, Shanghai, China +1

  15. CausalGraphX: A Counterfactual Graph Neural Network Framework for Explainable Systemic Risk Assessment

    Jul 15, 2026Rabimba Karanjai, Hemanth Madhavarao, Lei Xu +1Causal Representation LearningAI Risk Management

    University of Houston · PayPal Inc. · Kent State University

  16. Measuring What Matters: A Unified Evaluation Framework for GNN Explainability

    Jul 6, 2026Francesco Paolo Nerini, Mirko Zaffaroni, Paolo Baracco +2Explainability EvaluationGNN Explainability

    Sapienza University of Rome, Rome, Italy · Intesa Sanpaolo AI Research, Turin, Italy · Anti Financial Crime Digital Hub, Turin, Italy +1

  17. Towards the Explainability of Temporal Graph Networks via Memory Backtracking and Topological Attribution

    Jul 4, 2026Yazheng Liu, Xi Zhang, Sihong Xie +1Temporal GNNsFeature Attribution

    Artificial Intelligence Trust, The Hong Kong University of Science and Technology (Guangzhou) · Key Laboratory of Trustworthy Distributed Computing and Service (MoE), Beijing University of Posts and Telecommunications

  18. Explaining Temporal Graph Neural Networks via Feature-induced Information Flow

    Jun 25, 2026Ping Xiong, Thomas Schnake, Klaus-Robert Müller +1Temporal GNNsFeature Attribution

    Berlin Institute for the Foundations of Learning and Data – BIFOLD, 10623 Berlin, Germany · Machine Learning Group, Technical University of Berlin, Berlin, Germany · Department of Chemistry, Chemical Physics Theory Group, University of Toronto, Toronto, Canada +5

  19. A Completion-Aware Framework for Impactful Counterfactual Explainability in Graph Neural Networks

    Jun 20, 2026Maria Myrto Villia, Filippos Gouidis, Theodore Patkos +1Counterfactual ExplanationsGNN Explainability

    Institute of Computer Science, Foundation for Research and Technology - Hellas (FORTH), Heraklion, Greece · Computer Science Department, University of Crete, Greece

  20. CIExplainer++: Generating Causal and Interpretable Explanations for Graph Neural Networks

    Jun 17, 2026Francisco Caldas, Sahil Satish Kumar, Ruben Belo +1Causal InterpretabilityGNN Explainability

    NOVA LINCS, NOVA School of Science and Technology, Lisbon, Portugal

  21. Forecasting Is Not Attribution: Localizing Decoder Bypass in Graph-Based Neural Marketing Mix Models

    Jun 10, 2026Yunbo Wang, Bolbi LiuGraph Structure LearningFeature Attribution

    Department of Electrical Engineering and Computer Science University of California, Irvine · AdsGency AI

  22. Beyond Soft Masks: Hard-Perturbation Mixup Explainer for Robust GNN Explainability

    Jun 4, 2026Jialiang Yin, Zheng Zhao, Linsey Pang +3OOD GeneralizationGNN Explainability

    Xi’an Jiaotong University · Xi’an, China · PayPal +2

  23. HiSE: A Lightweight Hierarchical Semantic Explainer for Heterogeneous Graph Neural Networks

    Jun 2, 2026Zongrui Li, Yuhang Zhao, Ying Zhao +3Feature AttributionHeterogeneous GNNs

    School of Artificial Intelligence, Jilin University · Mohamed bin Zayed University of Artificial Intelligence

  24. Can Subgraph Explanations Be Weaponized to Steal Graph Neural Networks?

    May 28, 2026Ojas Nimase, Jiate Li, Yue Zhao +1Graph Neural NetworksModel Extraction Attacks

    University of Southern California Los Angeles, USA · Florida State University Tallahassee, USA

  25. A Generalized Tikhonov Layer for Interpretable-by-design Graph Neural Networks

    May 27, 2026Nicolas Tremblay, Benjamin Ricaud, Filippo Maria BianchiGraph Neural NetworksGraph Classification

    UiT the Arctic University of Norway · & CNRS, France · & NORCE Norwegian Research Centre

  26. What Molecular Structure Cannot Tell Us: A Taxonomy of Explainability Gaps in GNN-Based Drug Toxicity Prediction

    May 25, 2026Juergen DietrichMolecular Property PredictionGNN Explainability

  27. Aligning Molecular Graph Explanations with Chemical Identity via InChIfied Invariants

    May 23, 2026Emanuele Guidotti, Sara PuglioliMolecular Property PredictionInvariant Representation Learning

    University of Lugano, Switzerland · Philochem AG, Switzerland

  28. Relevant Walk Search for Explaining Graph Neural Networks

    May 22, 2026Ping Xiong, Thomas Schnake, Michael Gastegger +3Layer-Wise Relevance PropagationGNN Explainability

    Technische Universität Berlin (TU Berlin) · BIFOLD – Berlin Institute for the Foundations of Learning and Data · Freie Universität Berlin (FU Berlin) +4

  29. Efficient Higher-order Subgraph Attribution via Message Passing

    May 21, 2026Ping Xiong, Thomas Schnake, Grégoire Montavon +2Feature AttributionLayer-Wise Relevance Propagation

    Technische Universität Berlin (TU Berlin) · BIFOLD – Berlin Institute for the Foundations of Learning and Data · Department of Artificial Intelligence, Korea University, Seoul 136-713, Korea +2

  30. Ex-GraphRAG: Interpretable Evidence Routing for Graph-Augmented LLMs

    May 21, 2026Yoav Kor Sade, Arvindh Arun, Rishi Puri +2Graph Neural NetworksLLM Reasoning with Graphs

    Tel Aviv University · Institute for AI, University of Stuttgart · NVIDIA +1