*Graph Algorithms for Data Science*is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It's filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You'll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more.

Foreword by Michael Hunger.

**About the technology**

A graph, put simply, is a network of connected data. Graphs are an efficient way to identify and explore the significant relationships naturally occurring within a dataset. This book presents the most important algorithms for graph data science with examples from machine learning, business applications, natural language processing, and more.

**About the book**

*Graph Algorithms for Data Science*shows you how to construct and analyze graphs from structured and unstructured data. In it, you'll learn to apply graph algorithms like PageRank, community detection/clustering, and knowledge graph models by putting each new algorithm to work in a hands-on data project. This cutting-edge book also demonstrates how you can create graphs that optimize input for AI models using node embedding.

**What's inside**

**About the reader**

For data scientists who know machine learning basics. Examples use the Cypher query language, which is explained in the book.

**About the author**

**Tomaž Bratanic**works at the intersection of graphs and machine learning.

**Arturo Geigel**was the technical editor for this book.

**Table of Contents**

PART 1 INTRODUCTION TO GRAPHS

1 Graphs and network science: An introduction

2 Representing network structure: Designing your first graph model

PART 2 SOCIAL NETWORK ANALYSIS

3 Your first steps with Cypher query language

4 Exploratory graph analysis

5 Introduction to social network analysis

6 Projecting monopartite networks

7 Inferring co-occurrence networks based on bipartite networks

8 Constructing a nearest neighbor similarity network

PART 3 GRAPH MACHINE LEARNING

9 Node embeddings and classification

10 Link prediction

11 Knowledge graph completion

12 Constructing a graph using natural language processing technique