Approximation Algorithms for Reducing the Spectral Radius to Control Epidemic Spread

TL;DR

This paper introduces approximation algorithms to effectively reduce the spectral radius of networks, thereby controlling epidemic spread, with provable guarantees and practical improvements over existing heuristics.

Contribution

Develops new approximation algorithms for spectral radius reduction with theoretical guarantees and demonstrates their effectiveness through extensive experiments.

Findings

GreedyWalk algorithm achieves $O( ext{log}^2 n)$ approximation.

Primal-dual algorithm offers $O( ext{log} n)$ approximation with slower runtime.

Algorithms outperform prior heuristics in practical tests.

Abstract

The largest eigenvalue of the adjacency matrix of a network (referred to as the spectral radius) is an important metric in its own right. Further, for several models of epidemic spread on networks (e.g., the `flu-like' SIS model), it has been shown that an epidemic dies out quickly if the spectral radius of the graph is below a certain threshold that depends on the model parameters. This motivates a strategy to control epidemic spread by reducing the spectral radius of the underlying network. In this paper, we develop a suite of provable approximation algorithms for reducing the spectral radius by removing the minimum cost set of edges (modeling quarantining) or nodes (modeling vaccinations), with different time and quality tradeoffs. Our main algorithm, \textsc{GreedyWalk}, is based on the idea of hitting closed walks of a given length, and gives an $O(\log^2{n})$-approximation, where $n$ denotes the number of nodes; it also performs much better in practice compared to all prior heuristics proposed for this problem. We further present a novel sparsification method to improve its running time. In addition, we give a new primal-dual based algorithm with an even better approximation guarantee ($O(\log n)$), albeit with slower running time. We also give lower bounds on the worst-case performance of some of the popular heuristics. Finally we demonstrate the applicability of our algorithms and the properties of our solutions via extensive experiments on multiple synthetic and real networks.