| 000 | 01244nam a22002177a 4500 | ||
|---|---|---|---|
| 005 | 20260710154014.0 | ||
| 008 | 260710s2024 |||||||| |||| 00| 0 eng d | ||
| 020 | _a9781617299469 | ||
| 040 | _cPK-LaUMT | ||
| 082 |
_a006.31 _bBRA-G |
||
| 100 | 1 |
_aBratanic, Tomaz _913546 |
|
| 245 | 1 | 0 |
_aGraph algorithms for data science : _bwith examples in Neo4j / _cTomaž Bratanič |
| 260 |
_aShelter Island : _bManning, _c2024 |
||
| 300 | _axx, 330 p. | ||
| 500 | _aIncludes bibliographical references and index. | ||
| 520 | _aLabeled-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. | ||
| 546 | _aEng | ||
| 650 |
_aGraph algorithms _95563 |
||
| 650 | _aMachine learning | ||
| 942 | _cBK | ||
| 999 |
_c141376 _d141376 |
||