Update landing page for NeurIPS'22 Tutorial.

PiperOrigin-RevId: 491174446

examples/tutorials/neurips_2022/README.md

@@ -1,20 +1,27 @@
-# Graph Neural Networks in TensorFlow: A Practical Guide
-<center>NeurIPS'22 Tutorial</center>
+# <p align="center"> Graph Neural Networks in TensorFlow: A Practical Guide </p>
 
-<center> https://neurips.cc/Expo/Conferences/2022/workshop/63090 </center>
+## <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>
 
-### Abstract
+### 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 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.
+for learning from graph data and inferring missing information (such as
+predicting labels of nodes or imputing missing edges).
+
+#### Goals
+
+Thanks for your interest in our tutorial! 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](https://github.com/tensorflow/gnn), a library for
+working with graph structured data in TensorFlow.
 
 ### Schedule
 
@@ -23,11 +30,45 @@ of the tutorial will be based on TF-GNN.
 | 9:30        | Bryan Perozzi      | slides   | **Introduction:** Welcome and introduction. |
 | 9:40        | Sami Abu-El-Haija  | slides   | **Background:** Recap of Graph Neural Networks (GNNs) and problems they solve.  |
 | 10:00       | Sami Abu-El-Haija  | hands-on | **Tutorial 1:** Running TF-GNN on one machine. |
-| 10:20       |                    |          | **Break**  |
+| 10:20       | ---  | break    | *Break*  |
 | 10:30       | Neslihan Bulut     | slides   | **Modeling Guide**: Building blocks and modeling guidelines for crafting advanced custom GNN architectures. |
 | 10:50       | Neslihan Bulut     | hands-on | **Tutorial 2:** Implementing custom models, using TF-GNN’s modeling building blocks. |
-| 11:20       |                    |          | **Break**  |
+| 11:20       | --- | break    | *Break*  |
 | 11:30       | Brandon Mayer      | slides   | **Run at scale:** Challenges and solutions for running-at-scale |
 | 11:50       | Brandon Mayer      | hands-on | **Tutorial 3:** Running TF-GNN at industry scale over large graphs. |
 
-### Material
+### Tutorial Material
+
+Here's links to the material used in the presentation:
+
+1.  All Slides (forthcoming)
+2.  Hands-on Tutorial Links
+  1.  Tutorial 1: [TF-GNN Basics (Node Classification)](https://github.com/tensorflow/gnn/blob/main/examples/tutorials/neurips_2022/code_tutorial_1_tfgnn_single_machine.ipynb)
+  2.  Tutorial 2: [TF-GNN Modeling (Graph Classification), Student version](https://github.com/tensorflow/gnn/blob/main/examples/tutorials/neurips_2022/neurips_student_tfgnn_graph_classification_mutag.ipynb)
+      1. [Teacher version](https://github.com/tensorflow/gnn/blob/main/examples/tutorials/neurips_2022/neurips_teacher_tfgnn_graph_classification_mutag.ipynb)
+  3.  Tutorial 3: Running TF-GNN at Scale
+
+### Additional Resources
+
+If you're interested in learning more about GNNs or TF-GNN, we recommend the
+following resources:
+
+-   Our paper
+    [*TF-GNN: Graph Neural Networks in TensorFlow*](https://arxiv.org/pdf/2207.03522.pdf),
+    details the API design and background of the library.
+-   The in-depth notebook
+    [*OGBN-MAG end-to-end with TF-GNN*](https://github.com/tensorflow/gnn/blob/main/examples/notebooks/ogbn_mag_e2e.ipynb)
+    offers a deep dive on building heterogeneous graph models using TF-GNN.
+
+If you're interested in other work done by the
+[Graph Mining team](https://research.google/teams/graph-mining/) at Google, we
+have two 'background' workshops with recorded video that cover our work:
+
+1.  NeurIPS'20 Workshop -- https://gm-neurips-2020.github.io/
+2.  ICML'22 Workshop -- https://icml.cc/Expo/Conferences/2022/talkpanel/20446
+
+### Contact us
+
+If you have any questions about using TF-GNN, please feel free to drop a
+question on the [github repo](), or email us directly at `tensorflow-gnn AT
+googlegroups DOT com`.