Minimizing Seed Selection for Disseminating News with Probabilistic Coverage Guarantee

The spread of influence under Independent Cascade model and Linear Threshold model has been studied extensively in the literature. The two models describe the process through which individuals adopt a new product. We study the rumour model, that describes the process through which a news is spread in a network. We consider the task of identifying a collection of seed nodes of minimal size to spread a news such that, within a certain time threshold, the news can be spread to a target number of nodes with certain probability. We show that the problem is NP-hard. Moreover, we show that the function that calculates the probability of achieving the coverage threshold is not submodular, and hence conventional optimization techniques based on the submodularity property cannot be applied directly. Nevertheless, we present an algorithm that can provably approximate the size of the optimal seed set. In addition, we present a Monte-Carlo algorithm for choosing the best node that generates the greatest marginal increment in expected coverage. We compare the size of seed set with other baseline algorithms by using both real and synthetic networks. Our algorithm consistently outperforms other methods. For certain cases, the sizes of seed sets returned by our algorithm are less than a hundredth of that returned by the baseline algorithms compared.