Information diffusion is a natural phenomenon that information propagates from nodes to nodes over a social network. The behavior that a node adopts an information piece in a social network can be affected by different factors. Previously, many diffusion models are proposed to consider one or several fixed factors. The factors affecting the 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. In this work, our aim is to design a diffusion model in which factors considered are flexible to extend and change. We further propose a framework of learning parameters of the model, which is independent of factors considered. Therefore, with different factors, our diffusion model can be adapted to more scenarios of diffusion without requiring the modification of the diffusion model and the learning framework. In the experiment, we show that our diffusion model is very effective on the task of activation prediction on a Twitter dataset.
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