Neo4j: Applying Graph Algorithms on In-memory Graphs

Neo4j 4.3.6    |    Intermediate
  • 12 Videos | 1h 54m 37s
  • Includes Assessment
  • Earns a Badge
This course will introduce you to several graph algorithms in Neo4j's Graph Data Science library and explore how you can apply these to different types of graphs. You begin by building a little social network of people connected as friends. Then you will cover the steps involved in modeling friendships as undirected relationships in an in-memory graph and applying algorithms to this social network. You will use measures of centrality to identify highly connected nodes in a network. Next, you dive into community detection algorithms to find clusters of friends in a social network. From there, you will model a network as a graph with weighted edges then apply traversal algorithms on this graph, from finding shortest paths between nodes to breadth-first and depth-first traversals. Finally, you get a glimpse into the FastRP algorithm to transform nodes in your graph to vectors with a specific number of dimensions. After completing this course, you will know how to apply various graphic algorithms to extract meaningful information from a graph.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    create nodes and relationships from the contents of CSV files
    use different algorithms from the Graph Data Science library to compute the importance of each node in terms of connections
    identify clusters of closely knit communities in a network
    find individual nodes or clusters of nodes in a network which are not connected to one another
    compare and contrast the different techniques available to measure the importance of page references in a network of links
  • create a graph where each relationship has an attached weight
    find the shortest path between two nodes in network using the implementation of Dijkstra's algorithm in the Graph Data Science library
    use variant's of Dijkstra's algorithm to find multiple paths between the nodes in a network
    perform a breadth-first and depth-first traversal of a graph
    represent each node in your graph as a vector defined in a specified number of dimensions
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 53s
    UP NEXT
  • Playable
    2. 
    Loading Data from CSV Files
    11m 7s
  • Locked
    3. 
    Exploring Measures of Centrality
    10m 43s
  • Locked
    4. 
    Detecting Communities in a Graph
    11m 25s
  • Locked
    5. 
    Identifying Disconnected Components
    9m 57s
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    6. 
    Applying Page Rank and Article Rank
    12m 31s
  • Locked
    7. 
    Building a Weighted Graph
    10m 15s
  • Locked
    8. 
    Using Dijkstra's Shortest Path Algorithm
    12m 38s
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    9. 
    Finding Multiple Shortest Paths
    12m 13s
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    10. 
    Performing Graph Traversal
    9m 26s
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    11. 
    Transforming Nodes to Vectors with FastRP
    9m 17s
  • Locked
    12. 
    Course Summary
    2m 13s

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