MA214-5-SP-CO:
Network Analysis
2021/22
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Spring
Undergraduate: Level 5
Current
Monday 17 January 2022
Friday 25 March 2022
15
15 July 2021
Requisites for this module
MA114
(none)
(none)
(none)
(none)
BSC I1G3 Data Science and Analytics,
BSC I1GB Data Science and Analytics (Including Placement Year),
BSC I1GC Data Science and Analytics (Including Year Abroad),
BSC I1GF Data Science and Analytics (Including Foundation Year)
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 giving students an introduction to what a `network` is and how they appear in various real-world settings, as well as discussing their mathematical properties. starting from the initial description of networks, it leads to studying their properties and demonstrates programming tools which can be used to create and analyse networks. Finishing with describing how the properties and structures arise in real-world applications, how they can be analysed and what the analysis can tell us about them.
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. Introductory level programming will allow students to perform basic analyses and visualise network data in order to 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. Estimate network structure based on modelled and empirical data.
4. Understand how networks can be used to model complex systems from various disciplines.
5. Understand and produce analyses of network datasets.
6. 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. Erd"qs-Rényi networks); small-world networks (e.g. Watts-Strogatz networks); scale free networks (e.g. Barabási networks).
This module is computer-laboratory based, with lectures. The assessment is an individual assignment and a group report and presentation. Revision lectures will be given in the summer term for the examination.
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% |
| Coursework |
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 Dongjiao Ge, email: dongjiao.ge@essex.ac.uk.
Dr Dongjiao Ge
dongjiao.ge@essex.ac.uk
Yes
Yes
Yes
Dr Yinghui Wei
University of Plymouth
Available via Moodle
Of 1425 hours, 10 (0.7%) hours available to students:
1415 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
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