Scale-freeness under node removal: a finite-size scaling perspective

TL;DR

This paper investigates how scale-free network structures evolve under node removal using finite-size scaling analysis, revealing that different removal strategies affect the persistence of scale-invariance.

Contribution

It introduces a finite-size scaling approach to assess scale-free structure stability under various node removal strategies, highlighting discrepancies with degree distribution-based methods.

Findings

Random and hub-protecting removals largely agree across diagnostics.

Hub-preferential removal can cause networks to appear scale-free by degree distribution but not by scaling collapse.

Joint diagnostics provide a more complete picture of network structural degradation.

Abstract

In heterogeneous network systems such as ecological and social networks, structural stability depends on how connectivity changes under node removal, as different removal sequences can trigger distinct modes of systemic collapse. While robustness to random failures and targeted attacks has been extensively studied, most analyses have focused on connectivity loss or degree distribution, rather than on how scale-invariant organization emerges and evolves with system size. Here we examine how scale-free structure evolves under progressive degree-dependent node removal, systematically varying the hub-protection strength $\theta$. Starting from scale-free networks, we apply the recently developed finite-size scaling (FSS) analysis to node-removed networks and compare the results with those from Kullback-Leibler (KL) divergence-based classification. We find that under random ($\theta=0$) and hub-protecting removal ($\theta>0$), the two criteria largely agree, whereas under hub-preferential removal ($\theta<0$), networks may appear scale-free according to the KL criterion while failing the FSS test of scaling collapse. This discrepancy indicates that similarity to a reference degree distribution does not guarantee the persistence of scale-invariant organization across system sizes. The two diagnostics thus probe complementary aspects of network structure, and their joint use provides a more complete characterization of structural degradation.