We are excited to announce a call for contributed talks and posters/demos at the upcoming Stanford Graph Learning Workshop.

We are inviting two types of contributions (you can sign up for one or both):

Short talks: Give a 10-20 minute presentation on the main stage. Posters/demos: Present your poster/demo at a poster session. We expect the contributions of two types:

Significant Methodological Advancements, Open Source Tools, and Benchmarks: We are inviting contributions on the latest advancements in graph machine learning, geometric deep learning, and AI, as well as contributions in the areas of open source tools and benchmarks that enable the community to evaluate and compare the performance of different methods and models. Discussions about reproducibility, fairness, robustness, scalability, and efficiency are also encouraged.

Applications and Industrial Deployments: We are inviting contributions that highlight the use and impact of graph-based methods to real-world applications, focusing on the challenges faced during deployment, strategies adopted to overcome these challenges, and the ultimate impact achieved. Share your experiences, learnings, and insights to help the community understand the practical implications of graph machine learning.

To submit your proposal, please fill out the application form below by Oct 28th. Selected contributors will then be notified and will be invited to present in-person at the workshop. We look forward to receiving your contributions to this exciting workshop!