Graph Analytics Proficiency (Advanced Level)

Topics covered

• apply the degree centrality algorithm on a graph to get the level of connectedness of each node
• build a sub-graph containing a subset of elements from an already existing graph
• compare and contrast the different techniques available to measure the importance of page references in a network of links
• connect to an Aura database using Cypher shell and run queries against it
• create an in-memory graph using the native projection configuration for nodes and relationships
• create nodes and relationships from the contents of a CSV file
• define functions to present a directed as well as an undirected graph
• demonstrate the identification of the most and the least-connected nodes in a graph
• download and install Apache Spark and set up your IDE with GraphFrames
• export in-memory graphs to a set of CSV files containing data for nodes and relationships
• identify clusters of closely knit communities in a network
• illustrate how to find chains of connections as well as cycles in a GraphFrame
• implement Dijkstra's algorithm to compute the shortest path in a weighted graph
• list the relaxation techniques used to populate the distance table in Dijkstra's algorithm
• persist an in-memory graph to a Neo4j database
• represent each node in your graph as a vector defined in a specified number of dimensions
• search for patterns of relationships between the nodes in a Spark GraphFrame
• use different algorithms from the Graph Data Science library to compute the importance of each node in terms of connections
• use the page rank algorithm to compute a score for each node in a graph
• use variant's of Dijkstra's algorithm to find multiple paths between the nodes in a network