Graph Algorithms for Data Science
Manning Publications (Verlag)
978-1-61729-946-9 (ISBN)
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.
Tomaž Bratanič 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.
table of contents detailed TOC READ IN LIVEBOOK 1GRAPHS AND NETWORK SCIENCE: AN INTRODUCTION READ IN LIVEBOOK 2REPRESENTING NETWORK STRUCTURE - DESIGN YOUR FIRST GRAPH MODEL READ IN LIVEBOOK 3YOUR FIRST STEPS WITH THE CYPHER QUERY LANGUAGE READ IN LIVEBOOK 4CYPHER 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 LIVEBOOK APPENDIX A: ADJACENCY MATRIX
| Erscheinungsdatum | 03.02.2024 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 185 x 235 mm |
| Gewicht | 654 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Mathematik / Informatik ► Informatik ► Netzwerke | |
| Mathematik / Informatik ► Informatik ► Software Entwicklung | |
| Informatik ► Theorie / Studium ► Algorithmen | |
| Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
| ISBN-10 | 1-61729-946-4 / 1617299464 |
| ISBN-13 | 978-1-61729-946-9 / 9781617299469 |
| Zustand | Neuware |
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