Call for Papers

Learning on Graphs Conference, 2026

Table of Contents

Call For Papers

We welcome papers from areas broadly related to learning on graphs and geometry. The LoG conference has a proceedings track with papers published in Proceedings for Machine Learning Research (PMLR) and a non-archival extended abstract track.

Papers can be submitted through OpenReview using our LaTeX style files (download).

LoG 2026 is a fully in-person conference. At least one author of each accepted submission must attend in person.

Important Dates

(All deadlines are “Anywhere On Earth”.)

  • July 29, 2026: Abstract Submission Deadline (Both Tracks)

  • August 1, 2026: Full Paper Submission Deadline (Both Tracks)

  • August 8 - 22, 2026: Review Period

  • August 25, 2026: Rebuttal Stage Starts

  • September 1, 2026: Rebuttal Stage Ends, Authors-Reviewers Discussion Stage Starts

  • September 8, 2026: Authors-Reviewers Discussion Stage Ends

  • September 13, 2026: Final Decisions Released

  • October 15, 2026: Camera Ready Deadline

  • November 20 - 22, 2026 (Tentative): Conference Dates

Proceedings Track

Accepted proceedings papers will be published in the Proceedings for Machine Learning Research (PMLR) and are eligible for our proceedings spotlights. Full proceedings papers can have up to 9 pages with unlimited pages for references and appendix.

Submitted papers cannot be already published or under review in any other archival venue. Upon acceptance of a paper, at least one of the authors must join the conference, or their paper will not be included in the proceedings.

Extended Abstract Track

Extended abstracts can be up to 4 pages with unlimited pages for references and appendix. The top papers are chosen for our abstract spotlights. Authors of accepted extended abstracts (non-archival submissions) retain full copyright of their work, and acceptance to LoG does not preclude publication of the same material at another venue. Also, submissions that are under review or have been recently published are allowed for submission. Authors must ensure that they are not violating any other venue dual submission policies.

What qualifies as an extended abstract? Extended abstracts need to provide novel insights or enable future research with novel insights. This can be through presenting new ideas/ways of thinking, leading to insightful discussion and feedback, or dissemination of new valuable resources. We also welcome “non-traditional research artifacts” as submissions to the extended abstract track, such as papers highlighting novel datasets, insightful negative results, exciting preliminary results that warrant rapid dissemination, or reproducibility studies.

The Proceedings and Extended Abstracts track use the same OpenReview link for managing submissions. Please select the appropriate track when submitting.

Subject Areas

The following is a summary of LoG’s focus, which is not exhaustive. If you doubt that your paper fits the venue, feel free to contact pcs@logconference.org or logconference@googlegroups.com!

  • Expressive Graph Neural Networks
  • GNN architectures (transformers, new positional encodings, …)
  • Equivariant architectures
  • Statistical theory on graphs
  • Causal inference (structural causal models, …)
  • Algorithmic reasoning
  • Geometry processing
  • Robustness and adversarial attacks on graphs
  • Trustworthy graph ML (fairness, privacy, …)
  • Combinatorial Optimization and Graph Algorithms
  • Geometric and graph generative models (Diffusion, Flow Matching, …)
  • Graph Foundation Models
  • Graph Kernels
  • Graph Signal Processing/Spectral Methods
  • Graph Generative Models
  • Scalable Graph Learning Models and Methods
  • Graphs for Recommender Systems
  • Knowledge Graphs
  • Graph/Geometric ML for Computer Vision
  • Graph ML for Natural Language Processing and LLMs
  • Graph/Geometric ML for Molecules (molecules, proteins, drug discovery, …)
  • Graph ML for Security
  • Graph ML for Health
  • Graph/Geometric ML for Physical sciences
  • Graph ML Platforms and Systems
  • Self-supervised learning on graphs
  • Graph/Geometric ML Infrastructures (datasets, benchmarks, libraries, …)
  • Networks Analysis
  • Manifold learning
  • Neural manifold
  • Geometric optimization
  • Structured probabilistic inference

Preprint Policy

The existence of non-anonymous preprints (on arXiv or other online repositories, personal websites, social media) will not result in rejection. Authors may submit anonymized work to LoG that is already available as a preprint (e.g., on arXiv) without citing it.

Review Process

Submissions will be double-blind: reviewers cannot see author names when conducting reviews, and authors cannot see reviewer names. We use OpenReview to host papers and allow for public discussions that can be seen by all; comments that are posted by reviewers will remain anonymous. However, program chairs can know the reviewers’ identities and reviewers with particularly low-quality reviews can be excluded from future review processes (the review was flagged as low-quality and discussed by multiple area chairs and program chairs).

  1. Submissions are uploaded on OpenReview, publicly available, and official reviews are anonymous. Anybody can post comments that are publicly visible, or restrict visibility to e.g. reviewers or area chairs. We also recommend using Anonymous GitHub for authors to anonymize GitHub repositories.
  2. The rebuttal period starts on August 25th, during which authors can respond to reviews and post comments. Authors can also edit their paper during this period, but the changes will be visible to reviewers.
  3. After the rebuttal period ends on September 1st, author-reviewer discussion stage begins to allow for further clarification.
  4. After September 8th, there will be an internal discussion period amongst reviewers and ACs with the aim of summarising the review process, after which acceptance decisions are made.
  5. Accepted papers will be deanonymized (rejected ones can opt-out) after the notification on September 13th.