Unsupervised Spatial, Temporal and Relational Models for Social Processes
Unsupervised Spatial, Temporal and Relational Models for Social Processes George B. Davis February 2012 CMU-ISR-11-117 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Thesis Committee: Kathleen M. Carley (CMU, ISR), Chair Christos Faloutsos (CMU, CSD) Javier F. Pe~na(CMU, Tepper) Carter T. Butts (UCI, Sociology / MBS) Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy This work was supported in part by the National Science Foundation under the IGERT program (DGE- 9972762) for training and research in CASOS, the Office of Naval Research under Dynamic Network Analysis program (N00014- 02-1-0973, ONR N00014-06-1-0921, ONR N00014-06-1-0104) and ONR MURI N00014-08-1-1186, the Army Research Laboratory under ARL W911NF-08-R-0013, the Army Research Instituute under ARI W91WAW-07-C-0063, and ARO-ERDC W911NF-07-1-0317. Additional support was provided by CASOS - the Center for Computational Analysis of Social and Organizational Systems at Carnegie Mellon University. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied of the National Science Foundation, the Office of Naval Research, or the U.S. government. Keywords: Clustering, unsupervised learning, factor graphs, kernel density estimation Abstract This thesis addresses two challenges in extracting patterns from social data generated by modern sensor systems and electronic mechanisms. First, that such data often combine spatial, temporal, and relational evidence, requiring models that properly utilize the regularities of each domain. Sec- ond, that data from open-ended systems often contain a mixture between entities and relationships that are known a priori, others that are explicitly detected, and still others that are latent but significant in interpreting the data.
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