# Graph Neural Networks in TensorFlow: A Practical Guide
<center>NeurIPS'22 Tutorial</center>

<center> https://neurips.cc/Expo/Conferences/2022/workshop/63090 </center>

### Abstract

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 hereby infer missing information, such as,
predicting labels of nodes or imputing missing edges. The main goal of this
tutorial is to help practitioners and researchers to implement GNNs in a
TensorFlow setting. Specifically, the tutorial will be mostly hands-on, and will
walk the audience through a process of running existing GNNs on heterogeneous
graph data, and a tour of how to implement new GNN models. The hands-on portion
of the tutorial will be based on TF-GNN.

### Schedule

### Material
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PiperOrigin-RevId: 489020708
