A new model for preferential attachment scheme with time-varying parameters

TL;DR

This paper introduces a dynamic preferential attachment model with time-dependent parameters, providing estimation methods, asymptotic analysis, and change point detection techniques, supported by simulation studies.

Contribution

It extends the preferential attachment model to include time-varying parameters and develops estimation, asymptotic, and change point detection methods for this new framework.

Findings

Asymptotic properties of estimators established via CLT.

New statistic effectively detects change points.

Simulation results demonstrate method performance.

Abstract

We propose an extension of the preferential attachment scheme by allowing the connecting probability to depend on time t. We estimate the parameters involved in the model by minimizing the expected squared difference between the number of vertices of degree one and its conditional expectation. The asymptotic properties of the estimators are also investigated when the parameters are time-varying by establishing the central limit theorem (CLT) of the number of vertices of degree one. We propose a new statistic to test whether the parameters have change points. We also offer some methods to estimate the number of change points and detect the locations of change points. Simulations are conducted to illustrate the performances of the above results.