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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

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781839212611
Length 510 pages
Edition 1st Edition
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Author (1):
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 Scifo Scifo
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Scifo
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Toc

Table of Contents (18) Chapters Close

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

Adapting the model

In order to use our User class with Flask-login, we need to add a few methods to it:

  • is_authenticated: Determines whether the user authentication succeeded.
  • is_active: Can be used to deactivate users (email address not validated, subscription expired, and so on).
  • is_anonymous: This is another way to detect unauthenticated users.
  • get_id: Defines the unique identifier for each user.

Since these methods are common to almost all web applications, they have been implemented in a UserMixin, which we can add to our User class to access these methods. The only method we need a custom behavior for is the get_id method since our model does not have an id field; here, the primary key role is taken by the login field:

class User(StructuredNode, Flask_login.UserMixin):
# ... same as above

def get_id(self):
return self.login
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