HSE Perm Student Took Part in TMSA Summer School
Irina Shalyaeva, a first-year student of the Master’s programme 'Information Analytics in Enterprise Management' took part in the fifth international summer school ‘Theory and Methods of Exponential Random Graph Modeling’ (TMSA-2016). She shared her impressions of the event and the participants.
The fifth international summer school ‘Theory and Methods of Exponential Random Graph Modeling’ of the International Laboratory for Applied Network Research was held at HSE Moscow and dedicated to the theory and methods of exponential random graph modeling.
The main goal of the school was to introduce certain features of ERGM for researchers, doctoral students and undergraduates, to provide a better understanding of the formation and structure of networks by comparing all possible alternatives. The school programme focused on helping the participants use an integrated systems thinking approach to create theoretically driven, methodologically sound research projects.
Irina Shalyaeva, First-year student of the Master’s programme 'Information Analytics in Enterprise Management', shared her impressions of the school:
‘I think events like this are a very important and useful information platform for broadening our horizons, for gaining unique knowledge from experts in quickly and effectively and for discussing our own projects in a friendly academic environment. Although the project implemented as part of my master’s research at HSE Perm is not directly related to the modeling of social networks, I had a wonderful opportunity to get various opinions and advice, comments and new interesting ideas that we will try to implement.
Although the school was intended for people who already have some knowledge in the field of social network analysis, for me as a beginner in this area everything seemed clear and comprehensible. I would especially like to mention our teacher, Peng Wang from Swinburne University of Technology in Melbourne. All the theoretical material he presented to the school participants was supported by interesting examples from his practice, and we could apply tools we’d studied during classes to the data from our own research, or to data provided by the lecturer.
During such an intensive and tight schedule we still managed to form a comprehensive view about the discipline itself, to gain surprisingly deep knowledge and an interest in further self- education.