  The abundance of online user data has led to a surge of interests in
understanding the dynamics of social relationships using computational methods.
Utilizing users' items adoption data, we develop a new method to compute the
Granger-causal (GC) relationships among users. In order to handle the high
dimensional and sparse nature of the adoption data, we propose to model the
relationships among users in latent space instead of the original data space.
We devise a Linear Dynamical Topic Model (LDTM) that can capture the dynamics
of the users' items adoption behaviors in latent (topic) space. Using the time
series of temporal topic distributions learned by LDTM, we conduct Granger
causality tests to measure the social correlation relationships between pairs
of users. We call the combination of our LDTM and Granger causality tests as
Temporal Social Correlation. By conducting extensive experiments on
bibliographic data, where authors are analogous to users, we show that the
ordering of authors' name on their publications plays a statistically
significant role in the interaction of research topics among the authors. We
also present a case study to illustrate the correlational relationships between
pairs of authors.
