Module: Data Exploration Using Graph Theory

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Data Exploration Using Graph Theory
Module Code	CSC1051 (ITS: CA336)
Faculty	Engineering & Computing	School	Computing
NFQ level	8	Credit Rating	5

Description

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. Choose appropriate graph algorithms for solving graph theoretical problems. As part of this, students should be able to identify and critically reflect upon the assumptions informing ideas about expected, possible and desirable futures (Futures Literacy).
3. Appreciate the computational complexity of graph-theoretical solutions.
4. Write computer programs to solve graph theoretical problems. A core part of this is for students to be able to Identify and explain trends in specific domains and consider how they may shape future developments (Futures Literacy).
5. Develop problem-solving skills which are applicable to graph theory and general areas of data science. The students should be able to generate actionable strategies, individual or collective, relating to possible futures
6. Generate and classify ideas relating to expected, possible and desirable futures (Futures Literacy)
7. Demonstrate knowledge of key concepts and specific approaches relating to Futures Literacy.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	24	Formal Lectures
Fieldwork	36	Independent work solving graph theoretic computational problems.
Fieldwork	60	Independent learning.
Total Workload: 120

Section Breakdown
CRN	10233	Part of Term	Semester 1
Coursework	25%	Examination Weight	75%
Grade Scale	40PASS	Pass Both Elements	N
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Mark Roantree	Module Teacher

Type	Description	% of total	Assessment Date
Assessment Breakdown
In Class Test	Class Presentation to: Identify an important challenge. How do we want the future in healthcare, climate, education, air/sea travel (also assesses FL1 & FL2).	10%	Week 3
Assignment	Design and build a graph-based solution for a real-world case study. Choose a Futures Literacy concepts and reframe it as a research question you will answer in your project (FL3). Analyse your dataset using methods provided in text book to identify trends in your chosen challenge and explain how this information could shape future developments (FL4)	30%	Week 6
Assignment	Design a series of machine learning models which individually or collectively test your hypothesis for implementing a changed future (FL5) Validation of your hypothesis is an important aspect to this.	60%	n/a

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

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

Books:

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

Articles:
None

Other Resources

None