Proceedings Track
Full papers published in Proceedings for Machine Learning Research (PMLR), up to 9 pages with unlimited references and appendix; must not be published or under review elsewhere; at least one author must attend in person.
AI/CORE Unranked
LOG 2025
Learning on Graphs Conference
The Learning on Graphs Conference (LoG) is an annual research conference focused on machine learning on graphs and geometry, with an emphasis on high-quality peer review. LoG 2026 will be the fifth edition and the second in-person event, held in Boston, offering both archival and non-archival submission tracks alongside tutorials and local meetups.
Key deadlines
Verified September 9, 2026Abstract registration
August 23, 2025
AoE
Full paper
August 30, 2025
AoE
Conference timeline
Submission and decisions
Abstract registrationKey deadline
August 23, 2025 · AoE
Full paperKey deadline
August 30, 2025 · AoE
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Extended Abstract Track
Non-archival submissions up to 4 pages with unlimited references and appendix; authors retain copyright; allows previously published or under-review work; welcomes novel datasets, negative results, preliminary findings, and reproducibility studies.
NeurIPS 2025 Fast Track
Special track for NeurIPS 2025 submissions with average score ≥4.0; requires submission of original paper, reviews, meta-review, author response, and ethics statement; deadline September 29, 2025.
Research areas in scope
01
Subject Areas
Expressive Graph Neural NetworksGNN architectures (transformers, new positional encodings, …)Equivariant architecturesStatistical theory on graphsCausal inference (structural causal models, …)Algorithmic reasoningGeometry processingRobustness and adversarial attacks on graphsTrustworthy graph ML (fairness, privacy, …)Combinatorial Optimization and Graph AlgorithmsGeometric and graph generative models (Diffusion, Flow Matching, …)Graph Foundation ModelsGraph KernelsGraph Signal Processing/Spectral MethodsGraph Generative ModelsScalable Graph Learning Models and MethodsGraphs for Recommender SystemsKnowledge GraphsKnowledge Graphs and LLMsGraph/Geometric ML for Computer VisionGraph ML for Natural Language Processing and LLMsGraph/Geometric ML for Molecules (molecules, proteins, drug discovery, …)Graph ML for SecurityGraph ML for HealthGraph/Geometric ML for Physical sciencesGraph ML Platforms and SystemsSelf-supervised learning on graphsGraph/Geometric ML Infrastructures (datasets, benchmarks, libraries, …)Networks AnalysisManifold learningNeural manifoldGeometric optimizationStructured probabilistic inferenceLLMs and GraphsLLMs for Recommender SystemsLLMs and GeometryGraphs, Agents and Multi-Agent Systems
Policies worth checking twice
- Submissions are double-blind; author names are hidden from reviewers.
- Authors may submit anonymized work that is already available as a non-anonymous preprint without citing it.
- Proceedings track papers cannot be published or under review elsewhere.
- At least one author of each accepted paper must attend the conference in person.
- In exceptional cases (e.g., visa issues, financial hardship), remote presentation may be permitted upon prior approval.
- Authors must disclose use of generative AI in a dedicated appendix section if used for core research content (e.g., proofs, hypotheses, data generation, substantial drafting).
- Disclosure is not required for light uses such as spell checking or grammar correction.
- LLMs are not eligible for authorship.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Official conference siteCall for PapersCall for TutorialsProgram ChairsDownload Camera Ready Templatedownload
Last verified September 9, 2026