Learning Multiple Factors-Aware Diffusion Models in Social Networks

Information diffusion is a natural phenomenon occurring in social networks. The adoption behavior of a node toward an information piece in a social network can be affected by different factors, e.g., freshness and hotness. Previously, many diffusion models are proposed to consider one or several fixed factors. In fact, the factors affecting adoption decision of a node are different from one to another and may not be seen before. For a different scenario of diffusion with new factors, previous diffusion models may not model the diffusion well, or are not applicable at all. Moreover, uncertainty of information exposure intrinsically exists between two connected nodes, which causes modeling diffusion more challenge in social networks. In this work, our aim is to design a diffusion model in which factors considered are flexible to be extended and changed and the uncertainly of information exposure is explicitly tackled. Therefore, with different factors, our diffusion model can be adapted to more scenarios of diffusion without requiring the modification of the learning framework. We conduct comprehensive experiments to show that our diffusion model is effective on two important tasks of information diffusion, namely activation prediction and spread estimation.