Graph Algorithms for Data Science: http://amzn.to/4s41GJ5

I have said this for years: “All the world is a graph!” The natural data structure of the world is a graph, not rows and columns:

Butterfly effect Connected things (IoT) Context engineering Cyber networks Environmental phenomena Food webs Global markets Graph analytics Graph databases Knowledge graphs Linked data Network science Neuroscience & brain connectivity PageRank search algorithm Propagation of diseases Semantic metadata Six degrees of separation Social networks Supply chains etc.

Graph Algorithms for Data Science: With examples in Neo4j: 9781617299469: Computer Science Books @ Amazon.com

Source: https://www.amazon.com/dp/1617299464/ref=cm_sw_r_as_gl_api_gl_i_0XQW8JR5TBMKT76FSVHW?linkCode=ml1&tag=kirkdborne-20&linkId=d9b8aa49f1f2d024955d798654cbefa6 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 Scienceyou 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 Scienceis 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 technologyA 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 bookGraph Algorithms for Data Scienceshows 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 authorTomaž Bratanicworks at the intersection of graphs and machine learning.Arturo Geigelwas the technical editor for this book.Table of ContentsPART 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