Locally Adaptive Dynamic Networks

Abstract

Our focus is on realistically modeling and forecasting dynamic networks of face-to-face contacts among individuals. Important aspects of such data that lead to problems with current methods include the tendency of the contacts to move between periods of slow and rapid changes, and the dynamic heterogeneity in the actors' connectivity behaviors. Motivated by this application, we develop a novel method for Locally Adaptive DYnamic (LADY) network inference. The proposed model relies on a dynamic latent space representation in which each actor's position evolves in time via stochastic differential equations. Using a state space representation for these stochastic processes and Pólya-gamma data augmentation, we develop an efficient MCMC algorithm for posterior inference along with tractable procedures for online updating and forecasting of future networks. We evaluate performance in simulation studies, and consider an application to face-to-face contacts among individuals in a primary school.

References in corpus (8)

• Dynamics of person-to-person interactions from distributed RFID sensor networks
• What's in a crowd? Analysis of face-to-face behavioral networks
• Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys
• Contact patterns among high school students
• Dynamic stochastic blockmodels for time-evolving social networks
• Mitigation of infectious disease at school: targeted class closure vs school closure
• Modeling homophily and stochastic equivalence in symmetric relational data
• Latent Space Models for Dynamic Networks

Cited by in corpus (7)

• Mutually exciting point process graphs for modelling dynamic networks
• Dynamic latent space relational event model
• Modelling heterogeneity in Latent Space Models for Multidimensional Networks
• Estimation of dynamic networks for high-dimensional nonstationary time series
• Anomaly Detection in Large Scale Networks with Latent Space Models
• A Social Network Analysis of Articles on Social Network Analysis
• SimHawNet: A Modified Hawkes Process for Temporal Network Simulation