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Graph Machine Learning

You're reading from  Graph Machine Learning

Product type Book
Published in Jun 2021
Publisher Packt
ISBN-13 9781800204492
Pages 338 pages
Edition 1st Edition
Languages
Authors (3):
Claudio Stamile Claudio Stamile
Profile icon Claudio Stamile
Aldo Marzullo Aldo Marzullo
Profile icon Aldo Marzullo
Enrico Deusebio Enrico Deusebio
Profile icon Enrico Deusebio
View More author details

Table of Contents (15) Chapters

Preface Section 1 – Introduction to Graph Machine Learning
Chapter 1: Getting Started with Graphs Chapter 2: Graph Machine Learning Section 2 – Machine Learning on Graphs
Chapter 3: Unsupervised Graph Learning Chapter 4: Supervised Graph Learning Chapter 5: Problems with Machine Learning on Graphs Section 3 – Advanced Applications of Graph Machine Learning
Chapter 6: Social Network Graphs Chapter 7: Text Analytics and Natural Language Processing Using Graphs Chapter 8:Graph Analysis for Credit Card Transactions Chapter 9: Building a Data-Driven Graph-Powered Application Chapter 10: Novel Trends on Graphs Other Books You May Enjoy

Predicting missing links in a graph

Link prediction, also known as graph completion, is a common problem when dealing with graphs. More precisely, from a partially observed graph—a graph where for a certain pair of nodes it is not possible to exactly know if there is (or there is not) an edge between them—we want to predict whether or not edges exist for the unknown status node pairs, as seen in Figure 5.1. Formally, let be a graph where is its set of nodes and is its set of edges. The set of edges are known as observed links, while the set of edges are known as unknown links. The goal of the link prediction problem is to exploit the information of and to estimate . This problem is also common when dealing with temporal graph data. In this setting, let be a graph observed at a given timepoint , where we want to predict the edges of this graph at a given timepoint . The partially observed graph can be seen here:

Figure 5.1 – Partially observed graph with observed link  (solid lines) and unknown link  (dashed lines)

Figure 5.1 – Partially...

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