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.
Chathura’s doctoral dissertation, Models of Information Diffusion and the Role of Influence, examined how local behavioral rules and network structure combine to produce large-scale patterns of information spread. That question — how does individual behavior aggregate into population-level diffusion dynamics? — anchors this entire line of work.
Diffusion models describe that information spreads; influence measurement asks which actors and relationships actually drove that spread. Moving from one to the other requires methods that can attribute directional influence between individuals and communities, not just describe aggregate spread curves.
Through the DARPA Modeling Influence Pathways (MIPS) initiative — a sustained, cross-institutional collaboration between UCF, North Carolina State University, and MIT — Chathura developed methodology and an interactive dashboard using transfer-entropy-based methods to identify inter- and intra-community influence pathways in information-diffusion environments.
Transfer entropy is an information-theoretic measure of directed influence between time series: it quantifies how much knowing one actor’s past behavior reduces uncertainty about another actor’s future behavior, beyond what that actor’s own history already explains. Applied to social media activity, it lets researchers infer directional influence pathways without assuming a fixed network structure in advance.
To support this kind of analysis at scale, Chathura developed the Influence Network Generator (ING), an open-source Python package for constructing influence networks from online social media data, later adopted in further DARPA-funded diffusion research.
Chathura conceived the methodology and led algorithm design, system architecture, and visualization for the Transfer Entropy Interactive Dashboard, which was presented as the official final deliverable to the DARPA-MIPS program.
Chathura’s dissertation work established the diffusion-modeling foundation for this project; he was sole developer of ING and lead developer of the Transfer Entropy Interactive Dashboard, and is an author across the diffusion and influence publications listed below.
This project’s influence-measurement methods build on the behavioral premises first developed for the Multi-Action Cascade Model in the DARPA SocialSim work, and its statistical treatment of user behavior connects to the categorical functional data analysis project.
My contribution
Chathura's dissertation established the diffusion/influence modeling foundation for this line of work. He was sole developer of the Influence Network Generator and lead developer — conceiving the methodology and leading algorithm design, system architecture, and visualization — of the Transfer Entropy Interactive Dashboard presented as the official final deliverable to the DARPA-MIPS program.
Collaborators
Publications
2023
Jayalath, C., Gunaratne, C., Rand, W., Senevirathna, C., Garibay, I.
Advances in Complex Systems, 26(02), 2350005 (World Scientific)
Proposes a unified model generalizing threshold-based and probability-based approaches to information diffusion.
2019
Gunaratne, C., Baral, N., Rand, W., Garibay, I., Jayalath, C., Senevirathna, C.
arXiv preprint
Proposes a theory of extended working memory to explain patterns in online conversation dynamics.
2024
Abdidizaji, S., Baekey, A., Jayalath, C., Mantzaris, A., Garibay, O., Garibay, I.
International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 124–138, Springer Nature Switzerland
Uses transfer entropy and multiplex network measures to analyze how credibility and popularity shape news-sharing dynamics on X (Twitter).
2022
Garibay, O., Yousefi, N., Aslett, K., Baggio, J., Hemberg, E., Jayalath, C., Mantzaris, A., Miller, B., O'Reilly, U., Rand, W., Senevirathna, C., Garibay, I.
2022 IEEE 8th International Conference on Collaboration and Internet Computing (CIC), pp. 38–44, IEEE
Characterizes influence pathways across traditional and social media using entropy-based measures.
2020
Gunaratne, C., Baral, N., Rand, W., Garibay, I., Jayalath, C., Senevirathna, C.
Computational and Mathematical Organization Theory, 26, 255–276
Examines how information overload affects the dynamics of online conversations.
2021
Garibay, I., Oghaz, T., Yousefi, N., Mutlu, E., Schiappa, M., Scheinert, S., Anagnastopoulos, G., Bouwens, C., Fiore, S., Mantzaris, A., Murphy, J., Rand, W., Salter, A., Stanfill, M., Sukthankar, G., Baral, N., Fair, G., Gunaratne, C., Hajiakhoond, N., Jasser, J., Jayalath, C., Newton, O., Saadat, S., Seneviratna, C., Winter, R., Zhang, X.
Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas, pp. 153–169, Springer International Publishing
Presents the Deep Agent framework for studying the dynamics of information spread and evolution in social networks.
2019
Senevirathna, C., Gunaratne, C., Jayalath, C., Baral, N., Garibay, I.
Computational Social Science Society of the Americas (CSSSA), Santa Fe, NM
Presents evidence of influence hierarchies among participants in GitHub's cryptocurrency community.
2021
Baral, N., Gunaratne, C., Jayalath, C., Rand, W., Senevirathna, C., Garibay, I.
Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas, pp. 265–275, Springer International Publishing
Shows that negative gradients in influence lower the information-processing capacity of social networks.
2026
Jayalath, C., Champon, X., Rand, W., Jasser, J., Garibay, O., Garibay, I.
Data & Policy (Cambridge University Press), 8, e4
Compares targeted, community-based intervention strategies against population-wide responses for shaping information diffusion outcomes on social media.
2021
Senevirathna, C., Gunaratne, C., Rand, W., Jayalath, C., Garibay, I.
Entropy, 23(2), 160
Characterizes behavioral influence patterns in social media cascades using entropy-based measures.
—In preparation
Jayalath, C., Champon, X., Rand, W., Senevirathna, C., Garibay, I.
In preparation
Quantifies how network clustering coefficient affects outcomes of information diffusion models.
—In preparation
Jayalath, C., Champon, X., Dickey, D., Rand, W., Garibay, I.
In preparation
Investigates how the growth patterns of information cascades relate to the stability of an influencer's position over time.
Software
brandpy
Python package
Sole developer
A Python package interfacing with the Brandwatch API, enabling automated data retrieval and preprocessing for social-media research.
View (opens in a new tab)Influence Network Generator (ING)
Python package
Sole developer
An open-source Python package for generating influence networks from online social-media data, supporting large-scale diffusion modeling. Adopted in DARPA-funded research on information propagation. ING generates the transfer-entropy influence networks that the Transfer Entropy Interactive Dashboard consumes.
View (opens in a new tab)Transfer Entropy Interactive Dashboard
Research system
Lead developer
An analytical system implementing transfer-entropy-based methods to identify inter- and intra-community influence pathways in information-diffusion environments. Chathura conceived the methodology and led algorithm design, system architecture, and visualization. Presented as the official final deliverable to the DARPA-MIPS program.