constant distance from the actual source with a high probability, independent of the number of infected nodes and the time the snapshot is taken. Our simulation results show that for tree networks, the estimator produced by the reverse-infection algorithm is closer to the actual source than the one identified by the closeness centrality heuristic. We then further evaluate the performance of the reverse infection algorithm on several real world networks.'
author:
- 
bibliography:
- 'inlab-refs.bib'
title: 'Information Source Detection in the SIR Model: A Sample Path Based Approach'
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Introduction
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Diffusion processes in networks refer to the spread of information throughout the networks, and have been widely used to model many real-world phenomena such as the outbreak of epidemics, the spreading of gossips over online social networks, the spreading of computer virus over the Internet, and the adoption of innovations. Important properties of diffusion processes such as the outbreak thresholds [@MooNew_00] and the impact of network topologies [@GanMasTow_05] have been intensively studied.

In this paper, we are interested in the reverse of the diffusion problem: given a snapshot of the diffusion process at time $t,$ can we tell which node is the source of the diffusion? The answer to 