COMP8980 Computational Methods for Network Science

Not offered in 20276 units8000-level

Computational Methods for Network Science covers the essentials of using computational approaches to pose and answer social science research problems. In doing so it also covers a selected set of network algorithms in depth. This includes random graph models, homophily and friendship paradox, influence and contagion in networks, markets and network games, network resilence. Furthermore, it also teaches students about the ethics of doing data-driven social science research. The course equips the students with in-depth knowledge and hands-on experience in working with network data to study social processes at both the individual and aggregate levels. Graduates will be equipped with the technical, theoretical and conceptual skills and knowledge to start a budding career in this field of research.

2024not offered
2025not offered
2026not offered
2027not offered

Prerequisites in 2027

The handbook's wording is ambiguous; this is a person's reading of it

All of

enrolment in the Master of Applied Data Analytics
One of
- COMP6670Introduction to Machine Learning
- All of
  - One of
    COMP6710Structured Programming
    COMP6730Programming for Scientists
    COMP7230Introduction to Programming for Data Scientists
  - COMP8410Data Mining
  - STAT6039Principles of Mathematical Statistics

Why it's read this way: COMP8980 is the MADAN twin of COMP8880 (the two are incompatible), so the program condition applies to both of COMP8880's pathways.

Incompatible with COMP4880, COMP8880: you can't count both.

The handbook's original wording (2024–2027)

To enrol in this course you must have completed COMP6670 OR ( COMP6710 or COMP6730 or COMP7230 ) AND COMP8410 AND STAT6039 AND be enrolled in MADAN. Incompatible with COMP4880 and COMP8880 .

What it leads to in 2027

No course names COMP8980 in its 2027 prerequisites.

COMP8980 in the 2027 handbook on Programs and Courses