Latest Module Specifications
Current Academic Year 2025 - 2026
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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. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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 |
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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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List Books:
Articles: None | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Other Resources None | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||