Graph Classification

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Graph classification studies how to predict a label for an entire network, such as a molecule’s properties, a brain network’s condition, or a power grid’s failure state. Common approaches include graph kernels and handcrafted structural features, message-passing graph neural networks with pooling, and graph transformers; topological methods and language-model integrations offer additional ways to encode structure and semantics. Researchers also study explanations, uncertainty and abstention, and robustness when graph sizes, domains, or feature distributions change. A central tension is that complex learned models do not always outperform simple baselines, and performance can depend strongly on the graph domain, available attributes, and evaluation setup.

Latest papers

39 papers4 papers in the last four weeks, down 20% on the four weeks before. 0.0% of all new papers.

SAC-GCL: Structure-Aware Compression for Graph Contrastive Learning
Oct 27, 2026Muhammad Ifte khairul Islam, Tanvir Hossain, Esra AkbasGraph Contrastive Learning Graph Data Augmentation
Department of Computer Science, Texas A&M University- Texarkana, Texarkana,Texas, USA · Department Of Computer Science, Georgia State University, Atlanta, GA, USA · Department Of Computer Science, Georgia State University, Atlanta, Georgia, USA
Bayesian Evidence Accumulation for Graph-Based Learning in Dynamic Data-Driven Applications Systems
Oct 26, 2026Moises Ponce, Sarah Harkins Dayton, Ioannis Schizas +1Graph Classification Graph Neural Networks
University of Tennessee, Knoxville TN 37996, USA · US Army Combat Capabilities Development Command, Army Research Laboratory, Adelphi MD 20783, USA
Overlap-Aware Sub-hypergraph Pattern Frequencies for Hypergraph-Level Classification
Oct 24, 2026Nafiul Hasan Anik, Nazira Jesmin Lina, Md Tanvir Alam +1Hypergraph Learning Graph Classification
University of Dhaka
Persistence Positional Encoding: A Minimal Transformer Recipe for Persistence Diagrams
Oct 13, 2026Eunwoo Heo, Ganghyun Kim, Jaejun YooTopological Representation Learning Persistent Homology
Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology
MPGE: A Multi-Perspective Graph Explainer for Molecular Classification Explanation
Oct 8, 2026Mahtab SarvmailiGraph Counterfactual Explanations GNN Explainability
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
Global to Local: Topology-Preserving Adaptive Graph Pooling via Granular-Ball
Sep 4, 2026Sen Zhao, Gaojie Xu, Shuyin Xia +4Graph Pooling Hierarchical Representation Learning
Physics-Aware Random Walk Fingerprints for Scalable Power Grid Graph Classification
Sep 4, 2026Adnan AnwarGraph Classification Graph Representation Learning
School of Information Technology and a member of the Deakin Cyber Research & Innovation Hub, Deakin University, Melbourne, Australia
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 Continuous Attractor Neural Networks
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

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SAC-GCL: Structure-Aware Compression for Graph Contrastive Learning

Muhammad Ifte khairul Islam, Tanvir Hossain, Esra Akbas

Department of Computer Science, Texas A&M University- Texarkana, Texarkana,Texas, USA · Department Of Computer Science, Georgia State University, Atlanta, GA, USA · Department Of Computer Science, Georgia State University, Atlanta, Georgia, USA

Graph Contrastive Learning Graph Data Augmentation Graph Classification Graph Coarsening

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Graph Contrastive Learning (GCL) has achieved remarkable success in self-supervised representation learning by maximizing agreement between augmented views of a graph. Although augmented view generation is the central step in GCL, existing models rely on stochastic augmentation of the input graph, such as edge perturbation and node masking, which lack structural awareness and frequently destroy critical topology or introduce semantically inconsistent views. While recent adaptive methods improve upon random perturbations, they primarily operate on localized node or edge neighborhoods and fail to capture higher-order, macro-level structural organization crucial for graph classification. To address this limitation, we propose Structure-aware Compression for Graph Contrastive Learning (SAC-GCL), a novel framework that employs structural decomposition and graph compression as a principled view generation strategy. Moving beyond random augmentations, SAC-GCL leverages structural decomposition techniques to extract cohesive subgraphs and condense them into supernodes, constructing coarsened graph views that preserve local density and capture higher-level structural patterns while reducing noise and redundancy. We leverage multiple cohesive subgraph models to derive complementary views of the graph, each capturing distinct structural properties. To capture multi-resolution information, a hierarchical view encoder processes these compressed views by initializing super-node features with shared-weight cohesive subgraph embeddings. Our results from extensive experiments on six benchmark datasets demonstrate that structure-aware compression provides a powerful alternative to random perturbations, yielding more robust and discriminative graph representations for graph classification tasks.