Statistical inference framework for source detection of contagion processes on arbitrary network structures

被引：7

作者：

Antulov-Fantulin, Nino ^{[1
]}

Lancic, Alen ^{[2
]}

Stefancic, Hrvoje ^{[3
,4
]}

Sikic, Mile ^{[5
,6
]}

Smuc, Tomislav ^{[1
]}

机构：

[1] Rudjer Boskovic Inst, Informat Syst Lab, Div Elect, Zagreb, Croatia

[2] Univ Zagreb, Fac Sci, Dept Math, Zagreb 41000, Croatia

[3] Rudjer Boskovic Inst, Div Theoret Phys, Zagreb, Croatia

[4] Catholic Univ Croatia, Zagreb, Croatia

[5] Univ Zagreb, Fac Elect Engn & Comp, Dept Elect Syst & Informat Proc, Zagreb 41000, Croatia

[6] ASTAR, Bioinformat Inst, Singapore, Singapore

来源：

2014 IEEE EIGHTH INTERNATIONAL CONFERENCE ON SELF-ADAPTIVE AND SELF-ORGANIZING SYSTEMS WORKSHOPS (SASOW) | 2014年

关键词：

Contagion spreading; complex networks; source detection; EPIDEMIC SPREAD; COMPLEX; MODELS;

D O I：

10.1109/SASOW.2014.35

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

We introduce a statistical inference framework for maximum likelihood estimation of the contagion source from a partially observed contagion spreading process on an arbitrary network structure. The framework is based on simulations of a contagion spreading process from a set of potential sources which were infected in the observed realization. We present a number of different likelihood estimators for determining the conditional probabilities of potential initial sources producing the observed epidemic realization, which are computed in scalable and parallel way. This statistical inference framework is applicable to arbitrary networks with different dynamical spreading processes.

引用

页码：78 / 83

页数：6

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