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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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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

The friend-of-friend example

The friend-of-friend example is the most famous argument in favor of Neo4j when talking about performance. Since Neo4j is known to be incredibly performant at traversing relationships, contrary to other Database engines, we expect the response time of this query to be quite low.

Neo4j Browser displays the query execution time in the result cell:

It can also be measured programmatically. For instance, using the Neo4j Python driver from the Neo4j package, we can measure the total execution and streaming time with the following:

from neo4j import GraphDatabase

URL = "bolt://localhost:7687"
USER = "neo4j"
PWD = "neo4j"

driver = GraphDatabase.driver(URL, auth=(USER, PWD)

query = "MATCH (a:Person {id: 203749})-[:IS_FRIEND_WITH]-(b:Person) RETURN count(b.id)"

with driver.session() as session:
with session.begin_transaction() as tx:
result = tx.run(query)
summary = result.summary()
avail = summary.result_available_after...
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