# Neo4j: Applying Graph Algorithms on In-memory Graphs

Neo4j    |    Intermediate
• 12 videos | 1h 54m 37s
• Includes Assessment
Rating 3.7 of 3 users (3)
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

• 3.  Exploring Measures of Centrality
• 4.  Detecting Communities in a Graph
• 5.  Identifying Disconnected Components
• 6.  Applying Page Rank and Article Rank
• 7.  Building a Weighted Graph
• 8.  Using Dijkstra's Shortest Path Algorithm
• 9.  Finding Multiple Shortest Paths
• 10.  Performing Graph Traversal
• 11.  Transforming Nodes to Vectors with FastRP
• 12.  Course Summary

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