Growing networks with preferential addition and deletion of edges

TL;DR

This paper introduces a preferential attachment network model with edge deletion, analyzing how the degree distribution evolves and identifying conditions under which power-law behavior persists or transitions to exponential decay.

Contribution

It develops a recursive formula for the degree distribution in a network with edge deletion, revealing the conditions for power-law versus exponential decay in degrees.

Findings

For , degree distribution follows a power law with a specific exponent.

For , degree distribution decays exponentially.

There is a critical deletion probability threshold (=1/3) affecting the network's degree distribution.

Abstract

A preferential attachment model for a growing network incorporating deletion of edges is studied and the expected asymptotic degree distribution is analyzed. At each time step $t=1,2,\ldots$, with probability $\pi_1>0$ a new vertex with one edge attached to it is added to the network and the edge is connected to an existing vertex chosen proportionally to its degree, with probability $\pi_2$ a vertex is chosen proportionally to its degree and an edge is added between this vertex and a randomly chosen other vertex, and with probability $\pi_3=1-\pi_1-\pi_2<1/2$ a vertex is chosen proportionally to its degree and a random edge of this vertex is deleted. The model is intended to capture a situation where high-degree vertices are more dynamic than low-degree vertices in the sense that their connections tend to be changing. A recursion formula is derived for the expected asymptotic fraction $p_k$ of vertices with degree $k$, and solving this recursion reveals that, for $\pi_3<1/3$, we have $p_k\sim k^{-(3-7\pi_3)/(1-3\pi_3)}$, while, for $\pi_3>1/3$, the fraction $p_k$ decays exponentially at rate $(\pi_1+\pi_2)/2\pi_3$. There is hence a non-trivial upper bound for how much deletion the network can incorporate without loosing the power-law behavior of the degree distribution. The analytical results are supported by simulations.