CPP-SNS: A Solution to Influence Maximization Problem under Cost Control

As more and more people join social network, viral marketing on online social network becomes a new trend of advertising. Motivated by this, plenty of research focuseson how to maximize the information propagation, which is called the influence maximization problem. Traditional work has made significant progress on this topic. However all ad companies have marketing budget, the research of influence maximization problem should take account of cost control. Under the condition of cost control, we model each user´s cost of helping spread information as a feature of each node in the network. Then we modify several most widely studied algorithms to suit the new model. In this paper, a new algorithm called CPP-SNS is proposed, which selects seeds according to cost performance of nodes. Further improvements, based on strategy of partial node loading and submodular property of spread function, make CPP-SNS more effective in practical scenarios. Extensive experiments show this method has a good performance in different social networks. Based on results of our research, we also provide some advice for the practical marketing.