Graph Algorithms for Data Science: with examples in Neo4j
"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...
Gespeichert in:
| Beteilige Person: | |
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| Weitere beteiligte Personen: | |
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Shelter Island
Manning Publication Co.
[2024]
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| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781617299469/?ar |
| Zusammenfassung: | "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."-- |
| Beschreibung: | Includes index. - Description based on print version record |
| Umfang: | 1 Online-Ressource (xx, 330 Seiten) illustrations. |
| ISBN: | 9781617299469 1617299464 |
Internformat
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| spelling | Bratanič, Tomaž VerfasserIn aut Graph Algorithms for Data Science with examples in Neo4j Tomaž Bratanič ; foreword by Michael Hunger Shelter Island Manning Publication Co. [2024] 1 Online-Ressource (xx, 330 Seiten) illustrations. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes index. - Description based on print version record "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."-- Machine learning Data mining SQL (Computer program language) Scripting languages (Computer science) Apprentissage automatique Exploration de données (Informatique) SQL (Langage de programmation) Langages de script (Informatique) Hunger, Michael MitwirkendeR ctb 9781617299469 Erscheint auch als Druck-Ausgabe 9781617299469 |
| spellingShingle | Bratanič, Tomaž Graph Algorithms for Data Science with examples in Neo4j Machine learning Data mining SQL (Computer program language) Scripting languages (Computer science) Apprentissage automatique Exploration de données (Informatique) SQL (Langage de programmation) Langages de script (Informatique) |
| title | Graph Algorithms for Data Science with examples in Neo4j |
| title_auth | Graph Algorithms for Data Science with examples in Neo4j |
| title_exact_search | Graph Algorithms for Data Science with examples in Neo4j |
| title_full | Graph Algorithms for Data Science with examples in Neo4j Tomaž Bratanič ; foreword by Michael Hunger |
| title_fullStr | Graph Algorithms for Data Science with examples in Neo4j Tomaž Bratanič ; foreword by Michael Hunger |
| title_full_unstemmed | Graph Algorithms for Data Science with examples in Neo4j Tomaž Bratanič ; foreword by Michael Hunger |
| title_short | Graph Algorithms for Data Science |
| title_sort | graph algorithms for data science with examples in neo4j |
| title_sub | with examples in Neo4j |
| topic | Machine learning Data mining SQL (Computer program language) Scripting languages (Computer science) Apprentissage automatique Exploration de données (Informatique) SQL (Langage de programmation) Langages de script (Informatique) |
| topic_facet | Machine learning Data mining SQL (Computer program language) Scripting languages (Computer science) Apprentissage automatique Exploration de données (Informatique) SQL (Langage de programmation) Langages de script (Informatique) |
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