MA214-7-SP-CO:
Network Analysis
2022/23
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Spring
Postgraduate: Level 7
Current
Monday 16 January 2023
Friday 24 March 2023
15
10 October 2022
Requisites for this module
(none)
(none)
(none)
(none)
(none)
MSC G30512 Applied Data Science,
MSC G30524 Applied Data Science,
MSC G305JS Applied Data Science,
MSC G30612 Data Science and its Applications,
MSC G30624 Data Science and its Applications,
MSC G306JS Data Science and its Applications
Network theory is an active and dynamic area of research with applications across many fields, as such developments in the theory have appeared rapidly in recent years. The interdisciplinary nature of the topic has led to applications in biology, neurology, psychology, physical systems (such as power grids, transportation or communication systems), computer science, social sciences and economic theory.
This module is an introduction to networks and to their analysis. Starting from the initial description of networks, it leads to studying their mathematical properties and describing how such properties arise in real-world applications. Algorithms used to determine such properties and structures will be introduced and explored. Leading to an understanding of what network analyses can tell us about data sets.
The module will introduce students to the concept of a network along with descriptions of the most common types of networks, learning how and why they appear in various real-world systems. Students will develop an understanding of some of the common properties of networks such as centrality, connectedness and clustering and learn how to detect these structures. Algorithms used to determine properties will be introduced and discussed and will allow students to analyse complex network data, with the programming skills developed allowing them to visualise and present their findings in a clear manner.
On completing this course, students will:
1. Be able to implement algorithms for network analysis using Python and networkX.
2. Be able to calculate and interpret key features and properties of networks.
3. Have a systemic understanding of network structures and how to estimate these based on modelled and empirical data.
4. Develop an understanding of how networks can be used to model complex systems from various disciplines, identifying the characteristics of systems which indicate such an analysis is appropriate.
5. Critically analyse network datasets and interpret results.
6 . Understand algorithms used to determine properties and structures of networks, and have a critical awareness as to when they are appropriate to use.7 Be able to present and communicate the results of technical analyses in a clear manner.
Syllabus:
1. Introduction to network analysis using Python and networkX – basic commands for creating, importing, manipulating, analysing and visualizing networks.
2. Introduction to basic network theory: basic definition of a network; directed/undirected network; closed/open paths; cliques; common types of graph (bipartite, connected, cyclic etc.)
3. Common structures used in network theory: notion of centrality and the various versions of centrality (e.g. degree, closeness, betweenness); degree distribution; modularity; network density; clustering/communities.
4. Common types of networks and methods of constructing/identifying such networks and how these arise in real-world settings, including: random networks (e.g. Erdos-Renyi networks); small-world networks (e.g. Watts-Strogatz networks); scale free networks (e.g. Barabasi networks).
5. Common algorithms used to determine properties and structures of networks and the implications of these properties on real-world processes: breadth first search/width first search; clustering/community detection.
Teaching in the department will be delivered using a range of face to face lectures, classes and lab sessions as appropriate for each module. Modules may also include online only sessions where it is advantageous, for example for pedagogical reasons, to do so.
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Individual assignment |
|
25% |
| Coursework |
Group report |
|
25% |
| Written Exam |
Test |
|
50% |
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
Dr Joseph Bailey, email: jbailef@essex.ac.uk.
Dr Joe Bailey and Dr Andrew Harrison
jbailef@essex.ac.uk
Yes
Yes
Yes
Dr Yinghui Wei
University of Plymouth
Available via Moodle
Of 46 hours, 40 (87%) hours available to students:
0 hours not recorded due to service coverage or fault;
6 hours not recorded due to opt-out by lecturer(s), module, or event type.
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