Applied Graph Theory

Overview

Learn the basics of graph theory before modelling data as a distributed knowledge graph and thereafter undertaking complex network analysis to uncover hidden patterns and relationships in order to derive actionable intelligence from structured and unstructured data.

Course Details

This course provides a hands-on and in-depth exploration of graph theory applied to complex network analysis in order to uncover hidden patterns and relationships from structured and unstructured disparate datasets. This course explores in both theoretical and applied detail the major techniques used in graph theory and complex network analysis including bipartite graphs, similarity measures, extracting cliques and asymmetric relationships. This course also introduces distributed graph computing frameworks including Apache TinkerPop and the industry-standard Gremlin graph traversal language, along with useful tips for optimising the performance of massive graphs. Using these techniques, experienced senior data scientists and data engineers can model their disparate datasets as a distributed knowledge graph and perform complex network analysis that can be applied to a wide range of exciting use-cases including recommendation systems, fraud and criminal activity detection systems, DNA sequencing, location services, and social network analysis.

Course Modules

• 1. Introduction to Graph Theory
• 2. Basic Graph Structures Part 1
• 3. Basic Graph Structures Part 2
• 4. Apache TinkerPop and Gremlin
• 5. Distributed Graph Databases
• 6. Measuring Graphs and Centrality
• 7. Cliques and Communities
• 8. Measuring Similarity
• 9. Bipartite Graphs
• 10. Asymmetric Relationships
• 11. Gremlin Recipes
• 12. Optimising Massive Graphs

Requirements

• Introduction to Python or equivalent.
• Linear Algebra or equivalent.

Outcomes

• Knowledge of the major topics and techniques in applied graph theory and complex network analysis.
• The ability to model disparate structured and unstructured datasets as a distributed knowledge graph.
• The ability to perform complex network analysis in order to uncover hidden patterns and relationships.
• Intermediate knowledge of industry-standard distributed graph computing frameworks including Apache TinkerPop and the Gremlin graph traversal language.
• The ability to design, execute and optimise Gremlin queries applied to distributed graphs in Python.