Graph Algorithms for Data Science: With examples in Neo4j, Tomaž Bratanic

Practical methods for analyzing your data with graphs, revealing hidden connections and new insights.

Graphs are the natural way to represent and understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with concrete advice on implementation and deployment. You don’t need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects.

In

Graph Algorithms for Data Science

you will learn:

Labeled-property graph modeling

Constructing a graph from structured data such as CSV or SQL

NLP techniques to construct a graph from unstructured data

Cypher query language syntax to manipulate data and extract insights

Social network analysis algorithms like PageRank and community detection

How to translate graph structure to a ML model input with node embedding models

Using graph features in node classification and link prediction workflows

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

Creating knowledge graphs

Node classification and link prediction workflows

NLP techniques for graph construction

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

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