基于图信号处理的不规则网络感染分析
Infection Analysis on Irregular Networks through Graph Signal Processing
摘要：
在网络系统中，当节点受到感染时，由于内部随机故障或发展成流行病的传染性病毒，功能可能会受到严重威胁。给定代表节点状态（感染或健康）的网络快照，感染分析是指将流行病与随机故障区分开来，并收集信息以进行有效的对策设计。这种分析是具有挑战性的，由于不规则的网络结构，异构的流行病传播，和噪声观测。本文将网络快照视为一种图形信号，提出了一种基于图形信号处理的有效的感染分析方法。针对宏提出了一种基于图傅里叶变换（GFT）和图信号邻域特征的多检测指标，并提出了一种新的图小波--基于距离的图小波（DBGW）; 3）设计了一个基于机器学习的框架，该框架采用GFT谱或图小波系数作为感染分析的特征。DBGW还支持微观（节点级）感染分析，通过该分析可以提高流行病对策的性能。大量的仿真进行了各种网络设置，以证明所有提出的算法的有效性。
英文摘要：
In a networked system, functionality can be seriously endangered when nodes are infected, due to internal random failures or a contagious virus that develops into an epidemic. Given a snapshot of the network representing the nodes' states (infected or healthy), infection analysis refers to distinguishing an epidemic from random failures and gathering information for effective countermeasure design. This analysis is challenging due to irregular network structure, heterogeneous epidemic spreading, and noisy observations. This paper treats a network snapshot as a graph signal, and develops effective approaches for infection analysis based on graph signal processing. For the macro (network-level) analysis aiming to distinguish an epidemic from random failures, 1) multiple detection metrics are defined based on the graph Fourier transform (GFT) and neighborhood characteristics of the graph signal; 2) a new class of graph wavelets, distance-based graph wavelets (DBGWs), are developed; and 3) a machine learning-based framework is designed employing either the GFT spectrum or the graph wavelet coefficients as features for infection analysis. DBGWs also enable the micro (node-level) infection analysis, through which the performance of epidemic countermeasures can be improved. Extensive simulations are conducted to demonstrate the effectiveness of all the proposed algorithms in various network settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
