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

Antulov-Fantulin, Nino; Lančić, Alen; Štefančić, Hrvoje; Šikić, Mile; Šmuc, Tomislav (2014) Statistical inference framework for source detection of contagion processes on arbitrary network structures. In: Self-Adaptive and Self-Organizing Systems Workshops (8 September 2014 - 12 September 2014) London.

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Abstract

In this paper we introduce a statistical inference framework for estimating the contagion source from a partially observed contagion spreading process on an arbitrary network structure. The framework is based on a maximum likelihood estimation of a partial epidemic realization and involves large scale simulation of contagion spreading processes from the set of potential source locations. We present a number of different likelihood estimators that are used to determine the conditional probabilities associated to observing partial epidemic realization with particular source location candidates. This statistical inference framework is also applicable for arbitrary compartment contagion spreading processes on networks. We compare estimation accuracy of these approaches in a number of computational experiments performed with the SIR (susceptible-infected-recovered), SI (susceptible-infected) and ISS (ignorant-spreading-stifler) contagion spreading models on synthetic and real-world complex networks.

Item Type: Conference or workshop item published in conference proceedings (UNSPECIFIED)
Additional Information: The authors would like to thank Professor Yaneer Bar-Yam (New England Complex System Institute, Cambridge,MA 02142, USA) for valuable help in the early stages of the research.
Uncontrolled Keywords: contagion spreading ; complex networks ; source detection
Subjects: NATURAL SCIENCES > Physics
SOCIAL SCIENCES > Information and Communication Sciences
Divisions: Division of Electronics
Depositing User: Alen Vodopijevec
Date Deposited: 22 Dec 2016 13:29
Last Modified: 22 Dec 2016 13:34
URI: http://fulir.irb.hr/id/eprint/512
DOI: 10.1109/SASOW.2014.35

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