A recursive method for big network influence estimation

Influence maximization aims to find a set of highly influential nodes in a social network to maximize the spread of influence. The most difficult part of the problem is to estimate the influence spread of any seed set, which has been proved to be #P-hard. There is no efficient method to estimate the influence spread of any seed set till now. Thus, the most common way to obtain the approximate influence spread is Monte Carlo simulation and two popular simulating strategies are applied: one is propagation strategy, the other is snapshot strategy. The former only fits for particular seed set and the latter incurs heavy memory cost. In this paper, we present a new algorithm to estimate the influence spread of any seed set. Our algorithm recursively estimates the influence spread using reachable probabilities from node to node. Accordingly, we provide three strategies to start the recursion by integrating the memory cost and computing efficiency. Experiments demonstrate high performance of our influence estimation.