4 papers
Large Network limit for Latent Variable Process Models with Endogeneously Evolving Interactions among Multiple Types
Ankan Ganguly, Konstantinos Spiliopoulos
Motivated by the opinion and social dynamics literature, we rigorously study dynamically co-evolving latent space network models, where agent opinions evolve based on a latent stat…
Mean-Field Analysis of Latent Variable Process Models on Dynamically Evolving Graphs with Feedback Effects
Ankan Ganguly, Konstantinos Spiliopoulos, Daniel Sussman
We study the mean-field limit of a generic class of dynamic co-evolving latent space networks motivated by the social and opinion dynamics literature. Such models include agent…
Stochastic gradient descent-based inference for dynamic network models with attractors
Hancong Pan, Xiaojing Zhu, Cantay Caliskan +4
In Coevolving Latent Space Networks with Attractors (CLSNA) models, nodes in a latent space represent social actors, and edges indicate their dynamic interactions. Attractors are a…
Default Clustering in Large Pools: Large Deviations
Konstantinos Spiliopoulos, Richard B. Sowers
We study large deviations and rare default clustering events in a dynamic large heterogeneous portfolio of interconnected components. Defaults come as Poisson events and the defaul…