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You're reading from  Graph Data Science with Neo4j

Product typeBook
Published inJan 2023
Reading LevelIntermediate
PublisherPackt
ISBN-139781804612743
Edition1st Edition
Languages
Python
Tools
Neo4j
Concepts
Mobile Application Development
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Author (1)
Estelle Scifo
Estelle Scifo
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Estelle Scifo

Estelle Scifo possesses over 7 years experience as a data scientist, after receiving her PhD from the Laboratoire de lAcclrateur Linaire, Orsay (affiliated to CERN in Geneva). As a Neo4j certified professional, she uses graph databases on a daily basis and takes full advantage of its features to build efficient machine learning models out of this data. In addition, she is also a data science mentor to guide newcomers into the field. Her domain expertise and deep insight into the perspective of the beginners needs make her an excellent teacher.
Read more about Estelle Scifo

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Computing a node’s degree with GDS

We have studied the node degree metric and its distribution in the preceding chapter, Chapter 3, Characterizing a Graph Dataset. At that time, we computed the node’s degree using a Cypher query. GDS provides a procedure to perform the same computation, on a projected graph. We are now going to use this procedure, whose results are well known, in order to understand the different algorithm modes and configuration options.

All algorithm procedures from GDS use the same syntax:

gds.<algoName>.<executionMode>(<graphName>, <algoConfiguration>)

Here, the following applies:

  • algoName is the name of the algorithm. Note that some algorithms are included in an alpha or beta version, in which case they are accessible via gds.alpha.<algoName> or gds.beta.<algoName>.
  • executionMode is one of stream, write, mutate, estimate or stats, as defined in the GDS project workflow section.
  • graphName...
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Graph Data Science with Neo4j
Published in: Jan 2023Publisher: PacktISBN-13: 9781804612743
© 2023 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Estelle Scifo

Estelle Scifo possesses over 7 years experience as a data scientist, after receiving her PhD from the Laboratoire de lAcclrateur Linaire, Orsay (affiliated to CERN in Geneva). As a Neo4j certified professional, she uses graph databases on a daily basis and takes full advantage of its features to build efficient machine learning models out of this data. In addition, she is also a data science mentor to guide newcomers into the field. Her domain expertise and deep insight into the perspective of the beginners needs make her an excellent teacher.
Read more about Estelle Scifo

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