Graph Algorithms for Data Science Second Edition Version 4

Graph Algorithms for Data Science MEAP V04 Copyright welcome brief contents Chapter 1: Graphs and network science: An introduction 1.1 Introduction to graph theory 1.1.1 What is a graph? 1.2 How to spot a graph-shaped problem 1.3 Machine learning on graphs 1.4 Summary 1.5 References Chapter 2: Representing network structure - design your first graph model 2.1 Graph databases 2.1.1 RDF Graph Database 2.1.2 Labeled-property graph database 2.2 Designing your first labeled-property graph model 2.2.1 Follower network 2.2.2 User - Tweet network 2.2.3 Retweet network 2.2.4 Representing graph schema 2.3 Extracting knowledge from text 2.3.1 Links 2.3.2 Hashtags 2.3.3 Mentions 2.3.4 Final Twitter social network schema 2.4 Summary Chapter 3: Your first steps with the Cypher query language 3.1 Cypher query language clauses 3.1.1 RETURN clause 3.1.2 WITH clause 3.1.3 CREATE clause 3.1.4 MATCH clause 3.1.5 Set clause 3.1.6 REMOVE clause 3.1.7 DELETE clause 3.1.8 MERGE clause 3.2 Importing CSV files with Cypher 3.2.1 Cleanup the database 3.2.2 Twitter graph model 3.2.3 Unique constraints 3.2.4 LOAD CSV clause 3.2.5 Importing the Twitter social network 3.3 Summary Chapter 4: Cypher aggregations and social network analysis 4.1 Exploring the Twitter network with Cypher query language 4.1.1 Aggregating data with Cypher query language 4.1.2 Time aggregations 4.1.3 Filtering graph patterns 4.1.4 Counting relationships in Neo4j 4.2 Introduction to social network analysis 4.2.1 Node degree distribution 4.2.2 Neo4j Graph Data Science library 4.2.3 Graph Catalog and Native projection 4.2.4 Weakly Connected Component algorithm 4.2.5 Strongly Connected Components algorithm 4.2.6 Local clustering coefficient 4.2.7 Finding influencers with the PageRank algorithm 4.2.8 Drop named graph 4.3 Summary 4.4 References Chapter 5: Introduction to social network analysis 5.1 Followers network analysis 5.1.1 Node degree distribution 5.1.2 Introduction to Neo4j Graph Data Science library 5.1.3 Graph Catalog and Native projection 5.1.4 Weakly Connected Component algorithm 5.1.5 Strongly Connected Components algorithm 5.1.6 Local clustering coefficient 5.1.7 Finding influencers with the PageRank algorithm 5.1.8 Drop named graph 5.2 Summary 5.3 References Appendix A: Adjacency matrix 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 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. Foreword by Michael Hunger. 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 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 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 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 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 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. Its filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. Youll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. You dont 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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. 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. Youll 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, youll learn how to utilize graphs to upgrade your machine learning, including utilizing node embedding models and graph neural networks. About the reader For data scientists who know the basics of machine learning. Examples use the Cypher query language, which is explained in the book. About the author Toma Bratanic 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.