Spatial Temporal Exponential-Family Point Process Models for the Evolution of Social Systems
Year: 2016
Type: preprint
Abstract: We develop a class of exponential-family point processes based on a latent social space to model the coevolution of social structure and behavior over time. Temporal dynamics are modeled as a discrete Markov process specified through individual transition distributions for each actor in the system at a given time. We prove that these distributions have an analytic closed form under certain conditions and use the result to develop likelihood-based inference. We provide a computational framework to enable both simulation and inference in practice. Finally, we demonstrate the value of these models by analyzing alcohol and drug use over time in the context of adolescent friendship networks.
Source: arXiv (Cornell University)
Authors: EmBree, Joshua D., Handcock, Mark S.
Institutions: RAND Corporation, University of California, Los Angeles
Language: English
Subfield: Applied Mathematics
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, National Institutes of Health, Office of Naval Research, National Institute of Child Health and Human Development, California Center for Population Research, University of California, Los Angeles, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: G2507980080, G2998923865, G4154686922, Innovations in Network Modeling for HIV Prevention Studies, Scalable Model-Based Inference for Social Networks from Complex Sampling Designs, G6298873131, California Center for Population Research (CCPR) at UCLA, Statistical Methods for Respondent Driven Sampling Data, G8876996369, Valid Inference for Respondent Driven Sampling of Hidden Networked Populations
OpenAlex ID: W4293392353