Research theme 04
How can complex longitudinal, categorical, relational, and behavioral data be represented and analyzed statistically?
Categorical trajectories resolving into latent structure
Much of the data generated by social systems does not fit neatly into ordinary numeric time series: a social media user’s daily behavior is better described as a sequence of categorical states than as a continuous measurement. This theme is about building statistical representations that respect that structure instead of forcing it into a shape convenient for standard tools.
With collaborators, I have worked on categorical-valued functional data analysis —
representing densely observed categorical longitudinal data through latent-process
estimation and functional principal component analysis — and applied it to clustering
social media users by their behavioral trajectories. That work is implemented in the
open-source R package catfda and published in the Journal of the American
Statistical Association.
Projects
Many behavioral processes are observed as categorical trajectories rather than ordinary numeric time series. This work develops methods for representing and clustering densely observed categorical longitudinal data and applies them to social-media behavior.
Read the project ↗This line of work studies how information spreads through social systems and how influence can be measured across actors and communities. It spans my doctoral research, diffusion models, and DARPA MIPS work on influence pathways using transfer entropy and network-based methods.
Read the project ↗DARPA MIPs
This project asks which actors and communities actually drive the spread of information online. Under DARPA MIPs, I developed transfer-entropy-based methods and tools for identifying influence pathways within and between online communities.
DARPA MIPs (Modeling Influence Pathways), agreement HR00112290104 (PA-21-04-06). PI: Dr. Ivan Garibay, University of Central Florida.
Read the project ↗Publications
2026
Champon, X., Staicu, A., Weishampel, A., Jayalath, C., Rand, W.
Journal of the American Statistical Association
Develops categorical-valued functional data analysis methods for latent-process estimation and clustering of social media users' behavioral trajectories.
2024
Champon, X., Jayalath, C., Rand, W., Jasser, J., Garibay, I., Garibay, O.
57th Annual Hawaii International Conference on System Science (HICSS)
Applies functional data analysis methods to compare behavioral patterns across different social media communities.