Registry
Module Specifications
Archived Version 2020  2021
 
Description This module introduces graph theory, graph theoretic algorithms and graph databases. Students will be able to recognise dataanalysis problems with an underlying graphtheoretic component, and design and build software solutions to those problems.  
Learning Outcomes 1. Recognise graph theoretical components of real world dataanalysis problems. 2. State and prove core graph theoretical results. 3. Choose appropriate graph algorithms for solving graph theoretical problems. 4. Appreciate the computational complexity of graphtheoretical solutions. 5. Write computer programs to solve graph theoretical problems. 6. Develop problemsolving skills which are applicable to graph theory and general areas of data science.  
All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml 

Indicative Content and
Learning Activities Introduction Graph Analytics and Algorithms; Graph Processing, Databases, Queries, and Algorithms; Graph OLTP and OLAP. Graph Theory and Concepts Graph Structures; Types of Graphs; Types of Graph Algorithms; Pathfinding; Centrality Basic Algorithms Pathfinding and Graph Search Algorithms Building a Graph Database Application Nodes, Relationship Types, Facts as Nodes, Application Architecture, Clustering; Load Balancing; Testing; Performance Testing Graphs and Real world Applications Real world case studies Predictive Analysis Using Graphs Search; Path Finding; Predictive Modelling Clustering Using Graphs When to Use; Local clustering; Global clustering  
 
Indicative Reading List
 
Other Resources None  
Programme or List of Programmes  
Archives: 
