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The Neo4j Graph Platform – The #1 Platform for Connected Data
  • Products
    • Platform Overview → Neo4j graph technology products help the world make sense of data.
      • Graph Database
    • Neo4j Graph Database Self-managed, deploy anywhere
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    • Neo4j Graph Data Science Graph analytics and modeling platform
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    • Use Cases Fraud detection, knowledge graphs and more
      Knowledge Graphs Knowledge graphs are the force multiplier of smart data management and analytics use cases. Learn More
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Bite-Sized Neo4j for Data Scientists

  • Part 30: Introducing GDS 2.0!

  • Part 29: Using KNN with more sophisticated feature vectors (5/n)

  • Part 28: Using KNN to identify similar items of the Kaggle competition graph (4/n)

  • Part 27: Node similarity of a Kaggle competition graph (3/n)

  • Part 26: Creating a graph model of the Kaggle competition (2/n)

  • Part 25: Creating a graph for a Kaggle competition

  • Part 24: Why graphs? (6/6)

  • Part 23: PageRank done two ways (5/n)

  • Part 22: A side-by-side calculation of degree using SQL and Neo4j (4/n)

  • Part 21: An example of when querying a graph can be easier than SQL (3/n)

  • Part 20: …And compare it to a graph… (2/n)

  • Part 19: Starting with a SQL table…

  • Part 18: Bite-Sized Neo4j for Data Scientists – Putting Graph Embeddings into an ML Model

  • Graph Data Visualization for Data Scientists and Data Analysts | Neo4j Bloom

  • Part 17: Creating FastRP Graph Embeddings

  • Part 16: Using Strongly Connected Components to find Communities

  • Part 15: Community detection via Weakly Connected Components

  • Part 14: Community Detection with the Louvain Method

  • Part 13: Calculating Centrality

  • Part 12: Creating In-Memory Graphs with Native Projections

  • Part 11: Import RDF data from Wikidata

  • Part 10: Creating in-memory graphs with Cypher projections

  • Part 9: Cypher Queries 2

  • Part 8: Populating the Database from a JSON file

  • Part 7: Bite-Sized Neo4j for Data Scientists – Populating the Database with the neo4j-admin tool

  • Part 6: Bite-Sized Neo4j for Data Scientists – Populating the Database with LOAD CSV

  • Part 5: Bite-Sized Neo4j for Data Scientists – Populating the Database from Pandas

  • Part 4: Bite-Sized Neo4j for Data Scientists – Basic Cypher Queries (and with Google Colab)

  • Part 3: Bite-Sized Neo4j for Data Scientists – Using the Neo4j Python Driver

  • Part 2: Bited-Sized Neo4j for Data Scientists – Using the py2neo Python Driver

  • Part 1: Bite-Sized Neo4j for Data Scientists – Connect from Jupyter to a Neo4j Sandbox

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