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Hands-on Network Machine Learning with Scikit-Learn and Graspologic

Hands-on Network Machine Learning with Scikit-Learn and Graspologic

Introduction

Preface
Terminology

Foundations

1. The Network Machine Learning Landscape
2. End-to-end Biology Network Machine Learning Project

Representations

3. Properties of Networks as a Statistical Object
4. Why Use Statistical Models?
5. Learning Network Representations

Applications

6. Applications When You Have One Network
7. Applications for Two Networks
8. Applications for Many Networks

Next Steps

9. Where do we go from here?

Appendix

10. Representations (Extended)
- 10.1. Alternative Network Representations
11. Network Model Theory
12. Learning Representations Theory
- 12.1. Maximum Likelihood Estimate Theory
- 12.2. Spectral Method Theory
13. Applications (Extended)

Reference

Graspologic Documentation

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Applications (Extended)

13. Applications (Extended)#

This section contains useful results for the applications sections of the book.

Hypothesis Testing with coin flips,
Unsupervised learning, and
Bayes Plugin Classifier.

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12.2. Spectral Method Theory

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13.1. Hypothesis Testing with coin flips

By Eric Bridgeford, Alex Loftus, and Joshua Vogelstein
© Copyright 2022.