Event History Analysis of Dynamic Communication Networks

TL;DR

This paper introduces a novel statistical method for analyzing dynamic communication networks using multivariate counting processes and pseudo partial likelihood, providing asymptotic properties and demonstrating effectiveness through numerical experiments.

Contribution

It presents a new modeling approach for dynamic networks with a pseudo partial likelihood method and establishes its asymptotic properties.

Findings

Effective modeling of dynamic directed communication networks.

Asymptotic properties of the estimators are proven.

Numerical results validate the proposed method.

Abstract

Statistical analysis on networks has received growing attention due to demand from various emerging applications. In dynamic networks, one of the key interests is to model the event history of time-stamped interactions amongst nodes. We propose to model dynamic directed communication networks via multivariate counting processes. A pseudo partial likelihood approach is exploited to capture the network dependence structure. Asymptotic results of the resulting estimation are established. Numerical results are performed to demonstrate effectiveness of our proposal.