PROJECT TITLE :
Learning Multiple Factors-Aware Diffusion Models in Social Networks - 2018
Info diffusion is a natural phenomenon occurring in social networks. The adoption behavior of a node toward an data piece in a very social network can be laid low with completely different factors, e.g., freshness and hotness. Previously, several diffusion models are proposed to think about one or many fastened factors. In fact, the factors affecting adoption call of a node are different from one to another and could not be seen before. For a completely different state of affairs of diffusion with new factors, previous diffusion models could not model the diffusion well, or are not applicable in the slightest degree. Moreover, uncertainty of knowledge exposure intrinsically exists between two connected nodes, which causes modeling diffusion a lot of challenge in social networks. In this work, our aim is to design a diffusion model in which factors thought of are flexible to be extended and modified and the uncertainly of data exposure is explicitly tackled. So, with completely different factors, our diffusion model can be adapted to more scenarios of diffusion while not requiring the modification of the educational framework. We have a tendency to conduct comprehensive experiments to show that our diffusion model is effective on two necessary tasks of information diffusion, specifically activation prediction and unfold estimation.
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