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COMP4177: NETWORKS AND THEIR STRUCTURE

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Type	Open
Level	4
Credits	10
Availability	Available in 2024/2025
Module Cap	None.
Location	Durham
Department	Computer Science

Prerequisites

COMP2181 Theory of Computation

Corequisites

None

Excluded Combinations of Modules

None

Aims

To design structured networks to provide the communications fabric of distributed-memory multi-processors, networks-on-chips and data centre networks.
To introduce the theoretical and practical tools needed to analyse social and technological networks.

Content

Core aspects of interconnection networks: topology; routing; switching; flow control; packets; technology.
Graph theory: degree; cuts; bisections; paths; diameter; embeddings; automorphisms; symmetry.
Topologies: hypercubes; tori; k-ary n-cubes; cube-connected cycles.
Performance: traffic patterns; throughput; latency; path diversity; packaging; routing algorithms.
Modelling networks to make comparisons and predictions: random graphs; Milgram's small world experiment; Watts-Strogatz model; Kleinberg model.
Centrality measures: finding influential nodes in networks; using centrality measures to understand the community structure of networks.
Epidemics: how contagions spread in networks; models of diffusion; SIR model; epidemic threshold; SIS model.

Learning Outcomes

Subject-specific Knowledge:

On completion of the module, students will be able to demonstrate:
an awareness of the start-of-the-art in interconnection networks and network science
an in-depth knowledge of the key design principles of interconnection networks and their relation to current technology
a detailed knowledge of the structure of real world network and common approaches to building network models

Subject-specific Skills:

On completion of the module, students will be able to demonstrate:
an ability to reason with and apply theoretical methods within interconnection networks and network science
an ability to implement algorithms within interconnection networks
an ability to analyse network datasets and build and analyse network models

Key Skills:

On completion of the module, students will be able to demonstrate:
an ability to critically analyse and evaluate potential solutions within interconnection networks and network science
an ability to abstract real-world problems within interconnection networks and network science for scientific solution

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

Lectures enable the students to learn new material and engage in discussion.
Formative and summative assessments assess the application of methods and techniques.

Teaching Methods and Learning Hours

Activity	Number	Frequency	Duration	Total
lectures	20	2 per week	1 hour	20
preparation and reading				80
total				100

Summative Assessment

Component: Coursework		Component Weighting: 100%
Element	Length / Duration	Element Weighting	Resit Opportunity
Summative Assignment		100	No

Formative Assessment

Example formative exercises are given during the course.

More information

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