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Nov 27, 2022
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Minor tutorial site changes. #218

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9 changes: 5 additions & 4 deletions examples/tutorials/neurips_2022/README.md
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## <p align="center">**NeurIPS'22 Tutorial**</p>
### <p align="center">Monday, November 28th</p>
<p align="center">9:30 a.m. - 12:25 p.m. CST</p>
<h3 align="center"> <a href=https://neurips.cc/Expo/Conferences/2022/workshop/63090>Schedule and Livestream Link</a></h3>
<h3 align="center"> <a href=https://neurips.cc/Expo/Conferences/2022/workshop/63090>🎥 Schedule and Livestream Link 🎥</a></h3>

### Motivation

Graphs are general data structures that can represent information from a variety
of domains (social, biomedical, online transactions, and many more). Graph
Neural Networks (GNNs) are quickly becoming the de-facto Machine Learning models
for learning from graph data and inferring missing information (such as
predicting labels of nodes or imputing missing edges).
Neural Networks (GNNs) are an exciting way to use graph structured data inside
neural network models that have recently exploded in popularity. However,
implementing GNNs and running GNNs on large (and complex) datasets still raises
a number of challenges for machine learning platforms.

#### Goals

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