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

Manning Publications, 2024. — 353 p. — ISBN 9781617299469.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 workflowsGraph 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.
Purchase of the print book includes a free eBook in PDF, Kindle, and EPUB formats from Manning Publications.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 constructionAbout 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 grarhs
Graphs and network science: An introduction
Representing network structure: Designing your first graph model
Part 2 Social Network Analysis
Your first steps with Cypher query language
Exploratory graph analysis
Introduction to social network analysis
Projecting monopartite networks
Inferring co-occurrence networks based on bipartite networks
Constructing a nearest neighbor similarity network
Part 3 Graph Machine Learning
Node embeddings and classification
Link prediction
Knowledge graph completion
Constructing a graph using natural language processing technique