Statistical Methodologies for Temporal Networks

Networks that change with time are called dynamic or temporal networks. Examples of dynamic/temporal networks include social media networks and networks of disease spread.

Modelling these networks accurately will have huge societal benefits. This project is on exploring statistical methodologies available for temporal networks. The aim is to learn statistical methodologies that can handle temporal networks. Those concepts related to temporal networks will be explored using the statnet suite of R packages.

Nayani Ranasinghe

RMIT University

Nayani Darshila Ranasinghe is a student at RMIT university set to finish her bachelor degree in 2021. Following her passion for research, Nayani will undertake an honours year in 2022 where she will develop in-depth knowledge in statistics through research. She likes Bayesian statistics since thinks it provides people with the tools to update their beliefs in the evidence of new data.
Prior to migrating to Australia, Nayani worked as a middle school teacher in a government school in Colombo, Sri Lanka. Nayani continued to use her teaching skills that she worked as a maths tutor here since migration. Meanwhile, she worked hard to pursue excellence in her degree.
During this summer, Nayani will complete a six-week mathematical science project where she will study statistical methodologies that can handle temporal networks. She believes that this opportunity will give the head start for her to continue as a researcher in the future.

 

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