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The abundance of online user data has led to a surge of interests inunderstanding the dynamics of social relationships using computational methods.Utilizing users' items adoption data, we develop a new method to compute theGranger-causal (GC) relationships among users. In order to handle the highdimensional and sparse nature of the adoption data, we propose to model therelationships among users in latent space instead of the original data space.We devise a Linear Dynamical Topic Model (LDTM) that can capture the dynamicsof the users' items adoption behaviors in latent (topic) space. Using the timeseries of temporal topic distributions learned by LDTM, we conduct Grangercausality tests to measure the social correlation relationships between pairsof users. We call the combination of our LDTM and Granger causality tests asTemporal Social Correlation. By conducting extensive experiments onbibliographic data, where authors are analogous to users, we show that theordering of authors' name on their publications plays a statisticallysignificant role in the interaction of research topics among the authors. Wealso present a case study to illustrate the correlational relationships betweenpairs of authors.

Using Linear Dynamical Topic Model for Inferring Temporal Social Correlation in Latent Space