On Minimal Sets to Destroy the $k$-Core in Random Networks

TL;DR

This paper analyzes the minimal set of nodes whose removal destroys the $k$-core in random networks, introducing a new heuristic that outperforms existing methods and providing improved bounds on minimal contagious sets.

Contribution

It offers a detailed analysis of the corehd algorithm's performance on random networks and proposes a novel weak-neighbor heuristic that surpasses current local methods.

Findings

Corehd algorithm performance bounds improved

Weak-neighbor heuristic outperforms existing methods

Provides deterministic differential equations analysis

Abstract

We study the problem of finding the smallest set of nodes in a network whose removal results in an empty $k$-core; where the $k$-core is the sub-network obtained after the iterative removal of all nodes of degree smaller than $k$. This problem is also known in the literature as finding the minimal contagious set. The main contribution of our work is an analysis of the performance of the recently introduced corehd algorithm [Scientific Reports, 6, 37954 (2016)] on random networks taken from the configuration model via a set of deterministic differential equations. Our analyses provides upper bounds on the size of the minimal contagious set that improve over previously known bounds. Our second contribution is a new heuristic called the weak-neighbor algorithm that outperforms all currently known local methods in the regimes considered.