called distance centrality) heuristic. The infection closeness of a node is defined to be the inverse of the sum of distances to infected nodes and the infection closeness centrality heuristic is to claim the node with the maximum infection closeness as the source. Note that in [@ShaZam_10; @ShaZam_11; @ShaZam_12], the authors proved the node with the maximum infection closeness is the MLE on regular trees. For real world networks, our experiments also show that the reverse infection algorithm outperforms random guesses significantly. We then further evaluate the performance of the reverse infection algorithm on several real world networks.

Related Work
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There have been extensive studies on the spread of epidemics in networks based on the SIR model (see [@New_02; @PasVes_01; @MooNew_00; @GanMasTow_05] and references within). The work most related to this paper is [@ShaZam_10; @ShaZam_11; @ShaZam_12], in which the information source detection problem was studied under the SI model. [@LuoTay_12; @LuoTayLen_12] considers the problem of detecting multiple information sources under the SI model. This paper considers the SIR model, where infection nodes may recover, which can occur in many practical scenarios as we have explained. Because of node recovery, the information source detection problem under the SIR model differs 