The effect of preferential node deletion on the structure of networks that evolve via preferential attachment

TL;DR

This paper analytically investigates how preferential node deletion influences the structure of networks evolving through preferential attachment, revealing a phase transition between scale-free and exponential degree distributions depending on the growth-contraction balance.

Contribution

It introduces the PAPD model combining preferential attachment with node deletion, and analytically characterizes the resulting degree distribution phase transition.

Findings

For each m>0, a critical eta_c(m) determines the transition.

Power-law degree distribution occurs at pure growth (eta=1).

Exponential tail appears when contraction dominates (eta<1).

Abstract

We present analytical results for the effect of preferential node deletion on the structure of networks that evolve via node addition and preferential attachment. To this end, we consider a preferential-attachment-preferential-deletion (PAPD) model, in which at each time step, with probability $P_{\rm add}$ there is a growth step where an isolated node is added to the network, followed by the addition of $m$ edges, where each edge connects a node selected uniformly at random to a node selected preferentially in proportion to its degree. Alternatively, with probability $P_{\rm del}=1-P_{\rm add}$ there is a contraction step, in which a preferentially selected node is deleted and its links are erased. The balance between the growth and contraction processes is captured by the growth/contraction rate $\eta=P_{\rm add}-P_{\rm del}$. For $0 < \eta \le 1$ the overall process is of network growth, while for $-1\le\eta<0$ the overall process is of network contraction. Using the master equation and the generating function formalism, we study the time-dependent degree distribution $P_t(k)$. It is found that for each value of $m>0$ there is a critical value $\eta_c(m)=-(m-2)/(m+2)$ such that for $\eta_c(m)<\eta\le1$ the degree distribution $P_t(k)$ converges towards a stationary distribution $P_{\rm st}(k)$. In the special case of pure growth, where $\eta=1$, the model is reduced to a preferential attachment growth model and $P_{\rm st}(k)$ exhibits a power-law tail, which is a characteristic of scale-free networks. In contrast, for $\eta_c(m)<\eta<1$ the distribution $P_{\rm st}(k)$ exhibits an exponential tail, which has a well-defined scale.This implies a phase transition at $\eta=1$, in contrast with the preferential-attachment-random-deletion (PARD) model [B. Budnick, O. Biham and E. Katzav, J. Stat. Mech. 013401 (2025)], in which the power-law tail remains intact as long as $\eta>0$.