Seasonality in dynamic stochastic block models

TitleSeasonality in dynamic stochastic block models
Publication TypeConference Paper
Year of Publication2017
AuthorsRobinson, J, Doran, D
Conference NameProceedings of the International Conference on Web Intelligence
Pagination976–979
Date Published08/2017
PublisherACM
Conference LocationLeipzig, Germany
Abstract

Sociotechnological and geospatial processes exhibit time varying structure that make insight discovery challenging. This paper proposes a new statistical model for such systems, modeled as dynamic networks, to address this challenge. It assumes that vertices fall into one of k types and that the probability of edge formation at a particular time depends on the types of the incident nodes and the current time. The time dependencies are driven by unique seasonal processes, which many systems exhibit (e.g., predictable spikes in geospatial or web traffic each day). The paper defines the model as a generative process and an inference procedure to recover the seasonal processes from data when they are unknown. Evaluation with synthetic dynamic networks show the recovery of the latent seasonal processes that drive its formation.