2014/11/04 by Forough Arabshahi, Arabshahi, Forough, Furong Huang +7
Computer Science · Physics and Astronomy · #Advanced Text Analysis Techniques #Complex Network Analysis Techniques #Data Stream Mining Techniques #FOS: Computer and information sciences #Social and Information Networks (cs.SI) #cs.SI
paper · pdf · doi:10.48550/arxiv.1411.1132
openalex publication_date 2014/11/04 · arxiv created 2016/06/05 · arxiv updated 2016/06/07 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
Scalable probabilistic modeling and prediction in high dimensional multivariate time-series is a challenging problem, particularly for systems with hidden sources of dependence and/or homogeneity. Examples of such problems include dynamic social networks with co-evolving nodes and edges and dynamic student learning in online courses. Here, we address these problems through the discovery of hierarchical latent groups. We introduce a family of Conditional Latent Tree Models (CLTM), in which tree-structured latent variables incorporate the unknown groups. The latent tree itself is conditioned on observed covariates such as seasonality, historical activity, and node attributes. We propose a statistically efficient framework for learning both the hierarchical tree structure and the parameters of the CLTM. We demonstrate competitive performance in multiple real world datasets from different domains. These include a dataset on students' attempts at answering questions in a psychology MOOC, Twitter users participating in an emergency management discussion and interacting with one another, and windsurfers interacting on a beach in Southern California. In addition, our modeling framework provides valuable and interpretable information about the hidden group structures and their effect on the evolution of the time series.