LoG 2022 : Learning on Graphs
Call For Papers
*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 (coming soon). Papers are reviewed double-blind, and reviews are rated for their quality by authors and area chairs. The top reviewers receive high monetary rewards, as described below.
(All deadlines are “Anywhere On Earth”.)
September 9th, 2022: Abstract Submission Deadline (both Tracks)
September 16th, 2022: Submission Deadline (both Tracks)
October 20th, 2022: 2 Week Paper Revision Period Starts
November 3rd, 2022: Paper Revision Period Ends
November 24th, 2022: Final Decisions Released
November 30th, 2022: Camera Ready Deadline
December 9th, 2022: Conference Starts (Virtual, free to attend)
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.
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 firstname.lastname@example.org!
Expressive Graph Neural Networks
GNN architectures (transformers, new positional encodings, …)
Statistical theory on graphs
Causal inference (structural causal models, …)
Robustness and adversarial attacks on graphs
Combinatorial Optimization and Graph Algorithms
Graph Signal Processing/Spectral Methods
Graph Generative Models
Scalable Graph Learning Models and Methods
Graphs for Recommender Systems
Graph/Geometric ML for Computer Vision
Graph ML for Natural Language Processing
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
Trustworthy graph ML (fairness, privacy, …)
Graph/Geometric ML Infrastructures (datasets, benchmarks, libraries, …)