minimum infection eccentricity the Jordan infection centers. Therefore, the sample path based estimator is one of the Jordan infection centers.

-   We propose a low complexity algorithm, called reverse infection algorithm, to find the sample path based estimator in general graphs. In the algorithm, each infected node broadcasts its identity in the network, the node who first collect all identities of infected nodes declares itself as the information source, breaking ties based on the sum of distances to infected nodes. The running time of this algorithm is equal to the minimum infection eccentricity, and the number of messages each node receives/sends at each iteration is bounded by the degree of the node.

-   We analyze the performance of the reverse infection algorithm on $g$-regular trees, and show that the algorithm can output a node within a constant distance from the actual source with a high probability, independent of the number of infected nodes and the time the snapshot is taken.

-   We conduct extensive simulations over various networks to verify the performance of the reverse infection algorithm. The detection rate over regular trees is found to be around $60\%$, and is higher than that of the infection closeness centrality (or 