Graph Algorithms for Data Science : With Examples in Neo4j.

Format:

Book

Author/Creator:

Bratanic, Tomaž.

Language:

English

Subjects (All):

Machine learning.

Data mining.

SQL (Computer program language).

Scripting languages (Computer science).

Physical Description:

1 online resource (263 pages)

Edition:

1st ed.

Place of Publication:

New York : Manning Publications Co. LLC, 2024.

Summary:

"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. 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. about the technology Graphs reveal the relationships in your data. Tracking these interlinking connections reveals new insights and influences and lets you analyze each data point as part of a larger whole. This interconnected data is perfect for machine learning, as well as analyzing social networks, communities, and even product recommendations. about the book Graph Algorithms for Data Science teaches you how to construct graphs from both structured and unstructured data. You'll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights. The book explores common and useful graph algorithms like PageRank and community detection/clustering algorithms. Each new algorithm you learn is instantly put into action to complete a hands-on data project, including modeling a social network! Finally, you'll learn how to utilize graphs to upgrade your machine learning, including utilizing node embedding models and graph neural networks."-- Provided by Amazon.

Contents:

Part 1. Introduction to graphs. 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.

Notes:

Description based on publisher supplied metadata and other sources.

Includes index.

ISBN:

9781638350545

163835054X

OCLC:

1424746315