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Hands-On Graph Analytics with Neo4j

You're reading from Hands-On Graph Analytics with Neo4j Perform graph processing and visualization techniques using connected data across your enterprise

Product type Paperback

Published in Aug 2020

Publisher Packt

ISBN-13 9781839212611

Length 510 pages

Edition 1st Edition

Languages

Cypher

Tools

Neo4j

Concepts

Database Programming

Author (1):

Scifo

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Table of Contents (18) Chapters

Preface

1. Section 1: Graph Modeling with Neo4j

2. Graph Databases FREE CHAPTER

3. The Cypher Query Language

4. Empowering Your Business with Pure Cypher

5. Section 2: Graph Algorithms

6. The Graph Data Science Library and Path Finding

7. Spatial Data

8. Node Importance

9. Community Detection and Similarity Measures

10. Section 3: Machine Learning on Graphs

11. Using Graph-based Features in Machine Learning

12. Predicting Relationships

13. Graph Embedding - from Graphs to Matrices

14. Section 4: Neo4j for Production

15. Using Neo4j in Your Web Application

16. Neo4j at Scale

17. Other Books You May Enjoy

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Weighted nodes and relationships

In order to take into account the weight of a node or relationship, we can update the majority vote rule so that it does not only count the number of nodes with a given label but sums their weight. The selected label will then be the one with the highest sum of weights.

Let's consider the weighted version of the preceding graph illustrated in the following figure:

This time, in order to choose the label for node D, we have to take into account the weights of each edge connected to D:

Weight for the CR community = 1 (C) + 8 (D) = 9
Weight for the CG community = 1 (E) + 2 (G) = 3

Hence, in this weighted version of the graph, node D would belong to the CR community.

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Authors (1)

Scifo

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.

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