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

TL;DR

This paper introduces a statistical inference framework using maximum likelihood estimation to identify contagion sources in arbitrary networks, applicable to various contagion models and validated through computational experiments.

Contribution

It presents a novel, scalable inference framework for source detection in complex networks, accommodating different contagion models and partial observations.

Findings

High estimation accuracy demonstrated across multiple contagion models.

Effective in both synthetic and real-world network scenarios.

Framework adaptable to various contagion spreading processes.

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.