Wednesday, October 29, 2014

Temporal dynamics of social networks

Today the lab heard a talk by Dr. Bailey Fosdick, a new faculty member in Statistics at Colorado State University.  She spoke about temporal dynamics of social networks -- in this case, baboon troops in Kenya. The primatologist(s) that Dr. Fosdick works with observed that baboon troops go through fission events every 12-15 years and wanted to know if the members of the two resulting groups could be predicted.  In other words, this was the baboon version of the karate club data set.  Could we infer group membership in advance of a fission event?

Dr. Fosdick focused on one troop of baboons for which the data on social events covered 4.5 years.  Data was binned by month and focused on female members of the troop, based on the matriarchal social structure of the species. Dr. Fosdick focused on grooming events between individual females, creating a directed network with a count of grooming events per month.  She then mapped the baboon interactions into a latent space based on social closeness.  The position of each baboon in the latent space was based on the number of interactions she had with every other baboon in the previous month.  More precisely, Dr. Fosdick mapped each baboon to the latent space as a function of how the number of interactions differed from expected based on the covariates of mother-daughter relationship, amount of rainfall (and thus amount of insects that needed to be groomed), and relative social standing.  After pairwise distances between all baboons were calculated in this way, the position of each baboon in the latent space was visualized in 2D using multidimensional scaling. Thus, Dr. Fosdick was able to isolate the latent social space over time.  Stitching these 2D pictures together, she showed a movie of latent space which revealed the gradual separation of baboons into two troops.

The dynamics of social networks over time is an active area of research in network science.  My colleague Abigail Jacobs is also working on understanding the temporal dynamics of social networks.