Graph Analytics Proficiency

  • 20m
  • 20 questions
The Graph Analytics Proficiency benchmark will measure your ability to recall, recognize, and understand graph analytics concepts, graph databases, and Cypher Query Language for querying graph data and graph data science for identifying hidden relationships. A learner who scores high on this benchmark demonstrates that they have the required skills of Neo4j graph analytics and graph data science, graph data science with Spark, and to work independently in their projects.

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