# Seasonality in Dynamic Stochastic Block Models

## TL;DR

This paper introduces a new statistical model for dynamic networks with seasonal time dependencies, enabling the recovery of underlying seasonal processes from data, which is crucial for understanding time-varying sociotechnological and geospatial systems.

## Contribution

It proposes a novel dynamic stochastic block model incorporating seasonal effects and provides an inference method to recover these seasonal processes from data.

## Key findings

- Successful recovery of latent seasonal processes in synthetic networks
- Model captures time-varying edge formation driven by seasonal patterns
- Demonstrates effectiveness in understanding dynamic network structures

## 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.