Agenda

Sharmistha Guha (Texas A&M University) - Neural Network Gaussian Processes for Multiplex Networks: Joint Modeling of Dynamics and Attributes under Partial Observation

Abstract:

Terrorism networks are dynamic, multiplex, and often partially observed, demanding uncertainty-aware inference. This talk presents Dynamic Joint Learner, a Bayesian framework that jointly models the co-evolution of multiplex layers and node attributes using shared, time-varying latent factors. These latent trajectories are governed by neural network Gaussian processes, combining deep-network expressiveness with principled uncertainty propagation. The method supports predictive inference on hidden links, evolving organizational attributes (size, ideology, leadership, operational capacity), and emergent communities, including friend-foe structures. Simulation studies and an application to interactions among prominent terrorist organizations show improved performance over existing approaches for link prediction, attribute forecasting, and clustering, with calibrated uncertainty. The framework offers a practical toolkit for analysts working with partially observed, co-evolving networks and is broadly applicable beyond counter-terrorism.

The seminar can be attended also remotely, connecting to ZOOM.

Link Zoom: bit.ly/insem-2425
ID riunione:  880 2639 9452
Passcode: InSem-2425