Concentration inequalities for correlated network-valued processes with applications to community estimation and changepoint analysis

TL;DR

This paper develops concentration inequalities for correlated network-valued processes with asynchronous edge updates, enabling improved community detection and changepoint analysis in network time series.

Contribution

It introduces novel concentration bounds for lazy, correlated network processes and applies them to establish estimator consistency in community and changepoint detection.

Findings

Concentration inequalities hold for lazy, correlated network processes.

Laziness parameter influences estimation accuracy.

Simulation confirms theoretical predictions.

Abstract

Network-valued time series are currently a common form of network data. However, the study of the aggregate behavior of network sequences generated from network-valued stochastic processes is relatively rare. Most of the existing research focuses on the simple setup where the networks are independent (or conditionally independent) across time, and all edges are updated synchronously at each time step. In this paper, we study the concentration properties of the aggregated adjacency matrix and the corresponding Laplacian matrix associated with network sequences generated from lazy network-valued stochastic processes, where edges update asynchronously, and each edge follows a lazy stochastic process for its updates independent of the other edges. We demonstrate the usefulness of these concentration results in proving consistency of standard estimators in community estimation and changepoint estimation problems. We also conduct a simulation study to demonstrate the effect of the laziness parameter, which controls the extent of temporal correlation, on the accuracy of community and changepoint estimation.