Stanford Computer Forum 2021 Affiliates Meeting

Stanford Graph Learning Workshop

This workshop will bring together leaders from academia and industry to showcase recent methodological advances of Graph Neural Networks, a wide range of applications to different domains as well as machine learning frameworks and practical challenges for large-scale training and deployment of graph-based machine learning models.


Overview

Graphs are emerging as an abstraction to represent complex data, such as social networks, knowledge graphs, molecular graphs, biomedical networks, as well as for modeling 3D objects, manifolds, and source code. Machine learning, especially deep representation learning, on graphs is an emerging field with a wide array of applications from protein folding and fraud detection, to drug discovery and recommender systems.

In the Stanford Graph Learning Workshop, we will bring together leaders from academia and industry to showcase recent methodological advances of Graph Neural Networks. The workshop will present the leading graph machine learning framework and a wide range of graph machine learning applications to different domains. Additionally, the workshop will discuss practical challenges for large-scale training and deployment of graph-based machine learning models.

Registration

The Stanford Graph Learning Workshop will be held on Thursday, Sept 16 2021, 08:00 - 17:00 Pacific Time. The workshop will be part of the Stanford Computer Forum 2021 Affiliates Meeting.

Due to the COVID restrictions, the in-person event will be closed to the public. Meanwhile, the entire event will be live-streamed online, and free registrations to the public will be offered. If you wish to participate, please register with this link.
The event will be live-streamed on YouTube.

Workshop schedule

08:00 - 09:00   Registration & Breakfast

09:00 - 09:30   Jure Leskovec, Stanford -- Welcome and Overview of Graph Representation Learning[Slides]

09:30 - 10:15   Matthias Fey, TU Dortmund -- PyG 2.0: Advanced Representation Learning on Graphs[Slides]

10:15 - 10:45   Break

10:45 - 12:00   Industry panel

12:00 - 13:00   Lunch

13:00 - 13:20   Jan Eric Lenssen, TU Dortmund -- Applications to Graphics and Vision

13:20 - 13:40   Rex Ying, Stanford -- Applications to Fraud and Intrusion Detection[Slides]

13:40 - 14:00   Jiaxuan You, Stanford -- Applications to Financial Networks[Slides]

14:00 - 14:20   Hongyu Ren, Stanford -- Application to Knowledge Graphs[Slides]

14:20 - 14:40   Antoine Bosselut, Stanford -- Applications in Natural Language Processing[Slides]

14:40 - 15:00   Maria Brbic, Stanford -- Applications in Biomedicine[Slides]

15:00 - 15:30   Break

15:30 - 15:50   Jiaxuan You, Stanford -- GraphGym: Easy-to-use API for Graph Learning[Slides]

15:50 - 16:10   Weihua Hu, Stanford -- Open Graph Benchmark: Large-Scale Challenge[Slides]

16:15 - 17:00   Industry panel

17:00   Concluding remarks