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Hands-On Graph Neural Networks Using Python

You're reading from   Hands-On Graph Neural Networks Using Python Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch

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Product type Paperback
Published in Apr 2023
Publisher Packt
ISBN-13 9781804617526
Length 354 pages
Edition 1st Edition
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Author (1):
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Maxime Labonne Maxime Labonne
Author Profile Icon Maxime Labonne
Maxime Labonne
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Toc

Table of Contents (25) Chapters Close

Preface 1. Part 1: Introduction to Graph Learning
2. Chapter 1: Getting Started with Graph Learning FREE CHAPTER 3. Chapter 2: Graph Theory for Graph Neural Networks 4. Chapter 3: Creating Node Representations with DeepWalk 5. Part 2: Fundamentals
6. Chapter 4: Improving Embeddings with Biased Random Walks in Node2Vec 7. Chapter 5: Including Node Features with Vanilla Neural Networks 8. Chapter 6: Introducing Graph Convolutional Networks 9. Chapter 7: Graph Attention Networks 10. Part 3: Advanced Techniques
11. Chapter 8: Scaling Up Graph Neural Networks with GraphSAGE 12. Chapter 9: Defining Expressiveness for Graph Classification 13. Chapter 10: Predicting Links with Graph Neural Networks 14. Chapter 11: Generating Graphs Using Graph Neural Networks 15. Chapter 12: Learning from Heterogeneous Graphs 16. Chapter 13: Temporal Graph Neural Networks 17. Chapter 14: Explaining Graph Neural Networks 18. Part 4: Applications
19. Chapter 15: Forecasting Traffic Using A3T-GCN 20. Chapter 16: Detecting Anomalies Using Heterogeneous GNNs 21. Chapter 17: Building a Recommender System Using LightGCN 22. Chapter 18: Unlocking the Potential of Graph Neural Networks for Real-World Applications
23. Index 24. Other Books You May Enjoy

Exploring the PeMS-M dataset

In this section, we will explore our dataset to find patterns and get insights that will be useful to the task of interest.

The dataset we will use for this application is the medium variant of the PeMSD7 dataset [1]. The original dataset was obtained by collecting traffic speed from 39,000 sensor stations on the weekdays of May and June 2012 using the Caltrans Performance Measurement System (PeMS). We will only consider 228 stations across District 7 of California in the medium variant. These stations output 30-second speed measurements that are aggregated into 5-minute intervals in this dataset. For example, the following figure shows the Caltrans PeMS (pems.dot.ca.gov) with various traffic speeds:

Figure 15.1 – Traffic data from Caltrans PeMS with high speed (>60 mph) in green and low speed (<35 mph) in red

Figure 15.1 – Traffic data from Caltrans PeMS with high speed (>60 mph) in green and low speed (<35 mph) in red

We can directly load the dataset from GitHub and unzip it:

from io import BytesIO
from urllib.request...
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