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

Product type Paperback

Published in Apr 2023

Publisher Packt

ISBN-13 9781804617526

Length 354 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Deep Learning

Author (1):

Maxime Labonne

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Table of Contents (25) Chapters

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

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24. Other Books You May Enjoy

Preprocessing the CIDDS-001 dataset

In the last section, we identified some issues with the dataset we need to address to improve the accuracy of our model.

The CIDDS-001 dataset includes diverse types of data: we have numerical values such as duration, categorical features such as protocols (TCP, UDP, ICMP, and IGMP), and others such as timestamps or IP addresses. In the following exercise, we will choose how to represent these data types based on the information from the previous section and expert knowledge:

First, we can one-hot-encode the day of the week by retrieving this information from the timestamp. We will rename the resulting columns to make them more readable:

df['weekday'] = df['Date first seen'].dt.weekday
df = pd.get_dummies(df, columns=['weekday']).rename(columns = {'weekday_0': 'Monday','weekday_1': 'Tuesday','weekday_2': 'Wednesday', 'weekday_3': 'Thursday...

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Table of Contents (25) Chapters

Preprocessing the CIDDS-001 dataset

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Table of Contents (25) Chapters

Preprocessing the CIDDS-001 dataset

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