In previous works, the approaches mainly focused on network inference with temporal data. However, we have to doubt that whether the temporal information is precise in real world. Many networks do not have any records of precise time of infection like social networks do. For example, in a network of disease spreads, it’s quite time consuming to retract the exact infection time and even if we have the temporal info, it is probably incorrect and misleading for the reason that people react differently to the same disease, and the time they seek for medical help can be greatly influenced by subjective factors. The same condition also happens in other kinds of diffusion networks. The second defect is that they are quite time consuming if the algorithms are performed on lap-tops, usually several days. If they are applied, a high-performance computer or server is necessary.
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