Graph Classification

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2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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MPGE: A Multi-Perspective Graph Explainer for Molecular Classification Explanation
Oct 8, 2026Mahtab SarvmailiGNN Explainability Graph Classification
Dalhousie University Halifax, Nova Scotia, Canada
Shape irregularity of Life-Like Network Automaton rules as an indicator of classification performance
Oct 7, 2026Lucas C. S. Oliveira, Michiel Rollier, Jan Baetens +1Graph Classification Cellular Automata
Institute of Mathematics and Computer Science University of S˜ao Paulo S˜ao Carlos, 13566-590, Brazil · BionamiX, Dept. of Data Analysis and Mathematical Modelling Ghent University Ghent, 9000, Belgium · AICS, S˜ao Carlos Institute of Physics University of S˜ao Paulo S˜ao Carlos, 13566-590, Brazil
Topological Signatures of Cyber-Attack Classes in Natural Visibility Graph Representations of Network Traffic
Sep 22, 2026Ali Melih Kanca, Ilker TurkerNetwork Intrusion Detection Graph Classification
Department of Computer Engineering, Faculty of Computing and Informatics Sciences, Karabuk University, Karabuk, Türkiye
Repurposing Unified Topological Signatures for Graph Representation Learning
Sep 15, 2026Sanyam Sanjay Jain, Anshika Krishnatray, Aditya Sharma +1Graph Representation Learning GNN Expressivity
BITS Pilani, India
Evaluating Graph Neural Networks for Change-Criticality Classification in Maritime Navigation Charts
Sep 2, 2026Abhishek Potnis, Jacob ArndtGraph Neural Networks Graph Classification
Geospatial Science and Human Security Division, Oak Ridge National Laboratory, USA.
HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning
Aug 1, 2026Ruichen Xu, Jingxiang Qu, Wenhan Gao +5Graph Representation Learning Graph SSL
Department of Applied Mathematics and Statistics Stony Brook University Stony Brook, NY, USA · Department of Computer Science Stony Brook University Stony Brook, NY, USA · TikTok Bellevue, WA, USA +2
TopoFormer: Topology Meets Attention for Graph Learning
Jul 30, 2026Md Joshem Uddin, Astrit Tola, Cuneyt Gurcan Akcora +1Graph Representation Learning Graph Classification
Department of Mathematical Science The University of Texas at Dallas Richardson, TX 75080, USA · Department of Mathematics Florida State University Tallahassee, FL 32306, USA · AI Initiative University of Central Florida Orlando, FL 32816, USA
When Language Models Meet NeuroGraphs: Exploring Enhanced Agentic LLM Framework Towards Brain Network Analysis
Jul 24, 2026Jiaxing Li, Rui Dong, Muyao Tang +1LLM Interpretability LLM Agents
School of Computer Science and Engineering, Southeast University
Learning the Graphical Nature of Symmetries
Jul 13, 2026Rashid Barket, Enrico Grimaldi, Yacoub Hendi +3Graph Neural Networks Graph Representation Learning
Coventry University, Coventry, UK · Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy · Uppsala University, Uppsala, Sweden +5
path_boost: A Python Package for Interpretable Graph-Level Prediction using Path-Based Gradient Boosting
Jul 8, 2026Claudio Meggio, Johan Pensar, Riccardo De BinGradient-Boosted Decision Trees Graph Representation Learning
Department of Mathematics, University of Oslo, Oslo, 0371, Oslo, Norway.
Graph Classification via Network Usable Information: From Representation Evaluation to Structure Selection
Jul 3, 2026Abdullah Shaik, Anwar SaidGraph Representation Learning Graph Classification
Cyber Security Consultant Regulatory Risk & Forensic Deloitte Transaction and Business LLP Nashville, 37076 TN · Research Scientist Vanderbilt University Nashville, 37212 TN
Graph Neural Networks for the Graphical Bootstrap
Jul 3, 2026Rigers Aliaj, Gabriele Dian, Reza Doobary +1Graph Neural Networks High-Energy Physics
Deutsches Elektronen-Synchrotron DESY Hamburg, Germany · Independent Researcher United Kingdom · Department of Mathematical Sciences, Durham University Durham, United Kingdom
GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks
Jun 29, 2026Haoxin Sun, Yiqing Lin, Yajun Huang +3Language Model Pretraining Graph SSL
Fudan University Shanghai, China · ByteDance Shanghai, China
Learning Graphs through Continuous Information Entropy Fields
Jun 22, 2026Hui Cong, Bo Sun, Ziheng Jiao +1Graph Structure Learning Graph Neural Networks
School of Information Engineering, Chang’an University, Shaanxi, Xi’an, China · AUTHORERR: Missing \icmlaffiliation.
Controlled Dynamics Attractor Transformer
Jun 13, 2026Cheng Zhang, Minnan Luo, Zesheng Yang +3Energy-Based Models Graph Classification
School of Computer Science and Technology, MOEK-LINNS Lab, Xi’an Jiaotong University, Xi’an, Shaanxi, China · Department of Computer Science and Technology, Tsinghua University, Beijing, China
$k$ -Nearest Neighbors in Gromov--Wasserstein Space
Jun 9, 2026Kaitlyn Hohmeier, Nicolas Fraiman, Caroline MoosmuellerGraph Classification k-Nearest Neighbors
University of North Carolina at Chapel Hill, Department of Mathematics · University of North Carolina at Chapel Hill, Department of Statistics and Operations Research
AbstainGNN: Teaching Graph Neural Networks to Abstain for Graph Classification
May 29, 2026Xixun Lin, Zhiheng Zhou, Zhengyin Zhang +9Graph Neural Networks Selective Prediction
Institute of Information Engineering, Chinese Academy of Sciences · School of Mathematics and Statistics, Shandong University · China University of Mining and Technology-Beijing +6
Can Subgraph Explanations Be Weaponized to Steal Graph Neural Networks?
May 28, 2026Ojas Nimase, Jiate Li, Yue Zhao +1Graph Neural Networks Model Extraction Attacks
University of Southern California Los Angeles, USA · Florida State University Tallahassee, USA
A Generalized Tikhonov Layer for Interpretable-by-design Graph Neural Networks
May 27, 2026Nicolas Tremblay, Benjamin Ricaud, Filippo Maria BianchiGraph Neural Networks Graph Classification
UiT the Arctic University of Norway · & CNRS, France · & NORCE Norwegian Research Centre
OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data
May 15, 2026Louisa Cornelis, Johan Mathe, Louis Van Langendonck +2Graph Neural Networks Graph Representation Learning
UC Santa Barbara · Atmo, Inc. · Universitat Politècnica de Catalunya
CTQWformer: A CTQW-based Transformer for Graph Classification
May 10, 2026Zhan Li, Wuqing Yu, Yusen Wu +1Graph Representation Learning Quantum-Inspired ML
school of Artificial Intelligence, Beijing Normal University, Beijing, China
The Role of Node Features in Graph Pooling
May 7, 2026Jan von Pichowski, Alžbeta Hrabošová, Ingo Scholtes +1Graph Neural Networks Graph Pooling
Center for AI and Data Science University of Würzburg, Germany · Department of Computing Science Umeå University, Sweden
A Closed-Form Adaptive-Landmark Kernel for Certified Point-Cloud and Graph Classification
May 5, 2026Sushovan Majhi, Atish Mitra, Žiga Virk +1Point Cloud Classification Classification
Data Science, George Washington University, USA · Department of Mathematical Sciences, Montana Technological University, USA · Faculty of Computer and Information Science, University of Ljubljana, Slovenia +1
A Closed-Form Persistence-Landmark Pipeline for Certified Point-Cloud and Graph Classification
May 4, 2026Sushovan Majhi, Atish Mitra, Žiga Virk +1Point Cloud Classification Classification
Data Science, George Washington University, USA · Department of Mathematical Sciences, Montana Technological University, USA · Faculty of Computer and Information Science, University of Ljubljana, Institute IMFM, Slovenia +1
Path-Based Gradient Boosting for Graph-Level Prediction
Apr 26, 2026Claudio Meggio, Johan Pensar, Riccardo De BinGradient-Boosted Decision Trees Graph Representation Learning
Department of Mathematics, University of Oslo, Oslo, Norway
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
How Embeddings Shape Graph Neural Networks: Classical vs Quantum-Oriented Node Representations
Apr 16, 2026Nouhaila Innan, Antonello Rosato, Alberto Marchisio +1Quantum Machine Learning Quantum-Inspired ML
eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, UAE · Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, Rome, Italy · Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, NYUAD, Abu Dhabi, UAE
Evaluating Out-of-Distribution Robustness in Graph-Based Android Malware Classification: A New Principled Benchmark
Aug 8, 2025Ngoc N. Tran, Anwar Said, Waseem Abbas +2Distribution Shift Robustness Graph Representation Learning
Vanderbilt University, USA · Highmark Health, USA · University of Texas at Dallas, USA

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

Mahtab Sarvmaili

Dalhousie University Halifax, Nova Scotia, Canada

GNN Explainability Graph Classification Counterfactual Explanations

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Graph neural networks (GNNs) predict molecular properties from chemical graph data, but predictive accuracy does not explain how graph information supports an individual decision. A compact prediction-preserving rationale does not necessarily reveal which changes reverse the decision or which modifications the model tolerates. We propose the Multi-Perspective Graph Explainer (MPGE), unifying factual support, counterfactual sensitivity, and exemplar tolerance for a frozen classifier. The factual view, originally termed prototype (PT), seeks a compact retained edge set with the same label and required confidence. Counterfactual (CF) explanations seek bounded prediction-changing deletions; exemplar (EXE) explanations seek non-trivial bounded deletions that preserve the label and confidence. A shared constrained formulation connects prediction behavior, compactness, and edit cost, while separate objectives generate the three views. Our graph-classification extension of CF-GNNExplainer learns symmetric edge rankings and verifies discrete candidates, recording unsuccessful searches. A separate BBBP fragment backend returns RDKit-sanitized molecules. We evaluate the primary GCN implementation on MUTAG, Mutagenicity, AIDS, COX2_MD, and BBBP using semantic coverage, conditional quality, stability, and runtime. Successful factual masks retained 8.6%--15.5% of input edges on average across datasets; bounded counterfactual coverage was 4.8%--67.6%, and exemplar preservation coverage was 98.9%--100.0%. Exploratory controls reveal the influence of hard projection and retained node information. Quantitative comparisons and molecular visualizations characterize model support, sensitivity, and tolerance without treating them as validated chemical mechanisms.

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