Graph Algorithms for Data Science

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Sprache: Englisch

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Beschreibung

Graphs are the natural way to understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with practical examples and concrete advice on implementation and deployment. 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. 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.

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

Über den Autor

Toma Bratani is a network scientist at heart, working at the intersection of graphs and machine learning. He has applied these graph techniques to projects in various domains including fraud detection, biomedicine, business-oriented analytics, and recommendations.

Inhaltsverzeichnis

table of contents detailed TOC

READ IN LIVEBOOK1GRAPHS AND NETWORK SCIENCE: AN INTRODUCTION

READ IN LIVEBOOK2REPRESENTING NETWORK STRUCTURE - DESIGN YOUR FIRST GRAPH MODEL

READ IN LIVEBOOK3YOUR FIRST STEPS WITH THE CYPHER QUERY LANGUAGE

READ IN LIVEBOOK4CYPHER AGGREGATIONS AND SOCIAL NETWORK ANALYSIS

5 INFERRING NETWORKS AND MONOPARTITE PROJECTIONS

6 CONSTRUCT A GRAPH USING NLP TECHNIQUES

7 NODE EMBEDDINGS AND CLASSIFICATION

8 IMPROVE DOCUMENT CLASSIFICATION WITH GRAPH NEURAL NETWORKS

9 PREDICT NEW CONNECTIONS

10 KNOWLEDGE GRAPH COMPLETION

READ IN LIVEBOOKAPPENDIX A: ADJACENCY MATRIX

Details

Erscheinungsjahr:	2024
Fachbereich:	Datenkommunikation, Netze & Mailboxen
Genre:	Informatik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Seiten:	325
Inhalt:	Kartoniert / Broschiert
ISBN-13:	9781617299469
ISBN-10:	1617299464
Sprache:	Englisch
Einband:	Kartoniert / Broschiert
Autor:	Bratanic, Tomaz
Hersteller:	Manning Publications
Maße:	233 x 184 x 20 mm
Von/Mit:	Tomaz Bratanic
Erscheinungsdatum:	06.02.2024
Gewicht:	0,666 kg

preigu-id: 121342813