Extended Abstract
1 page, for preliminary results, work in progress, or emerging research ideas.
Machine Learning/CORE Unranked
GraphML 2026
ACML 2026 Workshop on Graph Machine Learning: Foundations, Frontiers, and Applications
GraphML 2026 is an ACML workshop focused on advancing graph machine learning, covering foundational research, theoretical frontiers, and real-world applications. It brings together researchers to present new ideas and foster collaboration in areas such as graph neural networks, foundation models, scalability, and trustworthy AI for graphs.
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Short Workshop Paper
Up to 4 pages, for more complete research with methods and experimental results. Page limits exclude references and supplementary material.
Research areas in scope
01
Foundations, Frontiers, and Applications
Graph Representation LearningGraph neural networksLearning on heterogeneous structuresTheoretical FoundationsExpressivityGeneralizationOptimizationGraph signal processingFormal analysisHigher-Order LearningHypergraph learningGeometric deep learningHigher-order relational modelingScalable And Robust ModelsScalable graph learningRobustness under shiftsDependable graph AI systemsGraph Foundation ModelsFoundation models for graphsMultimodal graph intelligenceTransfer across domainsApplicationsScienceEngineeringRecommendationKnowledge graphsDigital healthEducation
02
Subtopics under Foundations And Frontiers
Graph transformersGraph self-supervised and contrastive learningGraph generative modelsHeterogeneous graph learningDynamic and temporal graph learningTheoretical foundations of graph machine learningScalability and efficiency of graph learningRobust graph learning and learning under distribution shiftsExplainable and interpretable graph learningTrustworthy graph machine learningGraph foundation modelsGraph learning with large language modelsMultimodal graph learning and multimodal graph intelligenceEmerging paradigms and new directions in graph machine learning
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Subtopics under Graph Learning Applications
Knowledge graphsRecommender systemsSocial and complex networksBioinformatics and computational biologyHealthcareScientific discoveryIntelligent educationEngineering applicationsOther real-world applications of graph machine learning
Policies worth checking twice
- Submissions must follow the ACML 2026 LaTeX submission template and style file.
- All submissions must be written in English.
- Submission process follows a single-blind review process.
- Each submission will be reviewed by at least two reviewers based on relevance, originality, technical quality, clarity, and potential impact.
- Authors are encouraged to create or update their OpenReview profiles well in advance of the deadline.
- New profiles without an institutional email address may require moderation for up to two weeks.
- New profiles created with an institutional email address are activated automatically.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026