  Inspired by diachronic word analysis from the field of natural language
processing, we propose an approach for uncovering temporal insights regarding
user roles from social networks using graph embedding methods. Specifically, we
apply the role embedding algorithm, struc2vec, to a collection of social
networks exhibiting either "loyal" or "vagrant" characteristics derived from
the popular online social news aggregation website Reddit. For each subreddit,
we extract nine months of data and create network role embeddings on
consecutive time windows. We are then able to compare and contrast how user
roles change over time by aligning the resulting temporal embeddings spaces. In
particular, we analyse temporal role embeddings from an individual and a
community-level perspective for both loyal and vagrant communities present on
Reddit.
