Aspire     Graph Analytics     Graph Analytics Track 3: Graph Data Science with Neo4j

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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.

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Objectives

Neo4j: Applying Graph Algorithms on In-memory Graphs 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