Before revision:  Under the noise model, we estimate conditional expected marginal profit of a node by reverse influence sampling technique and propose an efficient algorithm , which could return a (1-exp({\epsilon} ' -1)) -approximation solution with high probability .
After revision:  Under the noise model, we estimate conditional expected marginal profit of a node by reverse influence sampling technique and propose an efficient algorithm , which could return a (1-exp({\epsilon} ' -1)) expected approximation ratio. Several experiments are conducted on six realistic datasets to compare our proposed policies with their corresponding non-adaptive algorithms and some heuristic adaptive policies .