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Module Specifications

Archived Version 2019 - 2020

Module Title
Module Code

Online Module Resources

NFQ level 8 Credit Rating 5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None

This module introduces graph theory, graph theoretic algorithms and graph databases. Students will be able to recognise data-analysis problems with an underlying graph-theoretic component, and design and build software solutions to those problems.

Learning Outcomes

1. Recognise graph theoretical components of real world data-analysis 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 graph-theoretical solutions.
5. Write computer programs to solve graph theoretical problems.
6. Develop problem-solving skills which are applicable to graph theory and general areas of data science.

Workload Full-time hours per semester
Type Hours Description
Lecture24Formal Lectures
Fieldwork36Independent work solving graph theoretic computational problems.
Fieldwork60Independent learning.
Total Workload: 120

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

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

Assessment Breakdown
Continuous Assessment% Examination Weight%
Course Work Breakdown
TypeDescription% of totalAssessment Date
Reassessment Requirement
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
Indicative Reading List

  • Mark Needham & Amy E. Hodler: 2019, Graph Algorithms: Practical Examples in Apache Spark & Neo4j, O'Reilly Publishaing, 978-1-492-057
  • Ian Robinson, Jim Webber & Emil Eifrem: 2013, Graph Databases, 2nd, 978-1-491-932
  • Rik Van Bruggen: 2014, Learning Neo4j, Packt Publishing, 978-1-84951-7
Other Resources

Programme or List of Programmes