Module Directory

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 aims of this module are:

• To 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.


• To develop an understanding of some of the common properties of networks such as centrality, connectedness and clustering and learn how to detect these structures.


• To develop introductory level programming that will allow students to perform basic analyses and visualise network data in order to present their findings in a clear manner.

By the end of the module, students will be expected to:

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.

Indicative syllabus:

Introduction to network analysis using Python and networkX:
Basic commands for creating, importing, manipulating, analysing and visualizing networks.

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.)

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.

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 will be delivered via:

• A range of face to face lectures, classes and lab sessions as appropriate.

The module may also include online-only sessions where it is advantageous, for example for pedagogical reasons, to do so.