Neo4j: Applying Graph Algorithms on In-memory Graphs
“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.”