Close
Description
Aims:
This module introduces the new research discipline of network science, which studies the structures and dynamics of complex networks in nature, society, and cyberspace, such as the protein interaction network in a cell, the scientist collaboration network in a research area, and the user network on online social media platforms.
This module covers a range of interesting topics, including methods to find the most influential node in a network, models to reproduce a network鈥檚 growth, and theories to explain spreading behaviours on a network.
Knowledge and skills obtained from this module are useful for data science and machine learning, as many large datasets are generated by networked systems and therefore can be better represented and understood from a network perspective.
Learning outcomes:
On successful completion of the module, a student will be able to:
- Define and calculate a wide range of network topological properties.
- Describe and explain the topological structure and evolution of different types of networks in nature, society, and cyberspace such as the Internet and the Web.
- Analyse the relation between network properties and network functions.
- Use network science algorithms, models, and tools to explore novel research questions related to large complex networks and online social networks.
Indicative content:
The following are indicative of the topics the module will typically cover:
- Complex networks in nature, society and cyberspace.
- Network topological properties.
- Random networks.
- Small-world networks.
- Scale-free networks.
- Generative network models.
- Rich-club phenomenon.
- Network mixing patterns.
- Network structural constraints.
- Network centrality measures.
- Internet topology and evolution.
- The structure of the World Wide Web.
- Network visualisation.
- Network community structure.
- Epidemic spreading in networks.
- Network controllability.
- Document networks.
- PageRank.
- Temporal networks.
- Spatial networks.
- Signed networks.
- Twitter botnets.
- Online social network analysis.
Requisites:
To be eligible to select this module as an option or elective, a student must: (1) be registered on a programme and year of study for which it is formally available; and (2) have strong competency in programming in Python/ Java/ R.
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
听