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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

Popularity and information spread

The most obvious way to define importance is with the influencer concept. Most influential nodes have many connections and, as such, are well placed to spread information. In the context of social networks, influencers often get offered advertising contracts with brands that want to harness their influencing power and let as many people as possible know about their products. With this in mind, nodes 3, 5, 6, and 9 in the previous diagram all have three connections, making them the most important nodes.

To go further and try to disentangle these four nodes, other methods have been proposed. The most famous of them is the PageRank algorithm, used by Google to rank the results of its search engine. PageRank updates the importance of a given node, taking into account the importance of its neighbors. If a web page has backlinks from an important page, its importance will increase, compared to another page that is referenced by less important pages...

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