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Multiscale modeling has yielded immense success on various machine learning tasks. However, it has not been properly explored for the prominent task of information diffusion, which aims to understand how information propagates along users in online social networks. For a specific user, whether and when to adopt a piece of information propagated from another user is affected by complex interactions, and thus, is very challenging to model. Current state-of-the-art techniques invoke deep neuraldoi:10.24963/ijcai.2020/464 dblp:conf/ijcai/LuoSP20 fatcat:vmb4rqtkzvcp5czqbvs7uignsq