01244nam a22002177a 4500 20260710154014.0 260710s2024 |||||||| |||| 00| 0 eng d 9781617299469 PK-LaUMT 006.31 BRA-G Bratanic, Tomaz 13546 Graph algorithms for data science : with examples in Neo4j / Tomaž Bratanič Shelter Island : Manning, 2024 xx, 330 p. Includes bibliographical references and index. 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. Eng Graph algorithms 5563 Machine learning BK 141376 141376 0 0 0 0 01 01 2026-07-10 0 006.31 BRA-G 153307 2026-07-10 15:40:29 2026-07-10 BK