For Semester 1 2019 the requirement to have completed COMP6670 is waived.
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.
Upon successful completion, students will have the knowledge and skills to:
- Demonstrate a thorough understanding of the fundamental principles of using computational approaches to formulate and answer social science questions.
- Demonstrate a working understanding in the ethical concerns of data drive analysis and experiments in human behavior.
- Apply network analysis algorithms in practical contexts.
- Analyze results from network algorithms, and articulate their limitations.
- Migrate insights from network analysis and simulation into new data and application scenarios.
- Communicate the process of formulating and solving computational social science problems to a team of professionals with computing and/or social sciences.
- Assignments (40) [LO null]
- Project (40) [LO null]
- Final Examination (20) [LO null]
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Up to 60 hours of total face-time, which includes interactions with lectures and tutors. Up to 60 hours of total preparation, repeat, assignment and practical exercise time.
Requisite and Incompatibility
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Commonwealth Support (CSP) Students
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- Unit value:
- 6 units
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