GNNSys 2021 : Workshop on Graph Neural Networks and Systems
Call For Papers
Call for Papers for GNNSys’21 — Workshop on Graph Neural Networks and Systems
DEADLINE EXTENDED TO MARCH 19!
We invite participation in the Graph Neural Networks and Systems Workshop, to be held in conjunction with MLSys 2021.
Graph Neural Networks (GNNs) have emerged as one of the hottest areas of research in the field of machine learning and artificial intelligence. The core idea is to explore the relationships among data samples to learn high-quality node, edge, and graph representations. In just the span of a few years, GNNs have expanded from mostly theoretical and small-scale studies to providing state-of-the-art solutions to many problems arising in diverse application domains. This includes domains that traditionally relied on graph learning (e.g., information retrieval, recommendations, fraud detection, knowledge representation), to science and engineering domains whose underlying data can be naturally represented via graphs (e.g., chemistry, bioinformatics, drug discoveries, material science, physics, circuit design), and to areas of science and engineering that have not traditionally been the domain of graph methods (e.g., computer vision, natural language processing, computer graphics, reinforcement learning).
GNN research and application present new and unique challenges to system designs. Industrial users and researchers share the same requirements in some of the requirements, but diverge in others. This landscape also rapidly evolves as new research results appear.
In the same spirit as MLSys, the goal of this workshop is to bring together experts working at the intersection of machine learning research and systems building, with a particular focus on GNN. Topics include, but not limited to:
* Systems for training and serving GNN models at scale
* System-level techniques to deal with complex graphs (heterogeneous, dynamic, temporal, etc.)
* Integration with graph and relational databases
* Distributed GNN training algorithms for large graphs
* Best practices to integrate with existing machine learning pipelines
* Specialized or custom hardware for GNN
* GNN model understanding tools (debugging, visualization, introspection, etc.)
* GNN applications to improve system design and optimizations
Through invited talks as well as oral and poster presentations by the participants, this workshop will showcase the latest advances in GNN systems and address challenges at the intersection of and GNN research and system design.
**Dual submissions:** The workshop proceedings will be published on the workshop website, but are considered non-archival for the purposes of dual submissions. We welcome work that has already been published or is under submission to a conference, and publishing at the workshop should not preclude you from submitting to conferences in the future. However, please check any conference policies as well.
* Xavier Bresson (National University of Singapore)
* Michael Bronstein from (Imperial College London/Twitter)
* Stefanie Jegelka (MIT)
* George Karypis (Univ of Minnesota/AWS ML)
* Petar Veličković (DeepMind)
* Zheng Zhang (NYU Shanghai/AWS ML)
## Submission Instructions
* Submissions can be up to 6 pages (not including references).
* All submissions must be in PDF and follow the the format outlined for [MLSys 2021](https://mlsys.org/Conferences/2021/CallForPapers)
* Submissions do not have to be anonymized
* Please submit your paper using the (EasyChair link) (https://easychair.org/conferences/?conf=gnnsys21)
## Important Dates
* Submission Deadline: March 19, 2021
* Acceptance Notifications: March 26, 2021
* Workshop: Friday, April 9, 2021
Contact us at (email@example.com)