Graph Algorithms for Data Science: With examples in Neo4j

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

Graphs are the natural way to represent and understand connecteddata. This book explores the most important algorithms andtechniques for graphs in data science, with concrete advice onimplementation and deployment. You don’t need any graph experienceto start benefiting from this insightful guide. These powerfulgraph algorithms are explained in clear, jargon-free text andillustrations that makes them easy to apply to your ownprojects.

In Graph Algorithms for Data Science you willlearn:
• Labeled-property graph modeling
• Constructing a graph from structured data such as CSV orSQL
• NLP techniques to construct a graph from unstructured data
• Cypher query language syntax to manipulate data and extractinsights
• Social network analysis algorithms like PageRank and communitydetection
• How to translate graph structure to a ML model input with nodeembedding models
• Using graph features in node classification and link predictionworkflows

Graph Algorithms for Data Science is a hands-on guideto working with graph-based data in applications like machinelearning, fraud detection, and business data analysis. It’s filledwith fascinating and fun projects, demonstrating the ins-and-outsof graphs. You’ll gain practical skills by analyzing Twitter,building graphs with NLP techniques, and much more.

About the technology
A graph, put simply, is a network of connected data. Graphs arean efficient way to identify and explore the significantrelationships naturally occurring within a dataset. This bookpresents the most important algorithms for graph data science withexamples from machine learning, business applications, naturallanguage processing, and more.

About the book
Graph Algorithms for Data Science shows you how toconstruct 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 byputting each new algorithm to work in a hands-on data project. Thiscutting-edge book also demonstrates how you can create graphs thatoptimize input for AI models using node embedding.

What's inside
• Creating knowledge graphs
• Node classification and link prediction workflows
• NLP techniques for graph construction