Steering Information Diffusion Dynamically against User Attention Limitation

As viral marketing in online social networks flourishes recently, a lot of attention has been drawn to the study of influence maximization in social networks. However, most works in influence maximization have overlooked the important role that social network providers (websites) play in the diffusion processes. Viral marketing campaigns are usually sold by websites as services to their clients. The websites can not only select initial sets of users to start diffusion processes, but can also have impacts throughout the diffusion processes by deciding when the information should be brought to the attention of individual users. This is especially true when user attention is limited, and the websites have to notify users about an item to bring it into the attention of users. In this paper, we study the diffusion of information from the perspective of social network websites. We propose a novel push-driven cascade (PDC) model, which emphasizes the role of websites during the diffusion of information. In the PDC model, the website "pushes" items to bring them to the attention of users, and whether a user is interested in an item is decided by her preference and the social influence from her friends. Analogous to the influence maximization problem on the traditional information diffusion models, we propose a dynamic influence maximization problem on the PDC model, which is defined as a sequential decision making problem for the website. We show that the problem can be formalized as a Markov sequential decision problem, and there exists a deterministic Markovian policy that is an optimal solution for the problem. We develop an AO algorithm that finds the optimal solution for the problem, and a heuristic online search algorithm, which has similar effectiveness, but is significantly more efficient. We evaluate the proposed algorithms on various real-world datasets, and find them significantly outperform the baselines.