Abstract
We propose in this paper a new generative model for graphs that uses a latent space approach to explain timestamped interactions. The model is designed to provide global estimates of activity dates in historical networks where only the interaction dates between agents are known with reasonable precision. Experimental results show that the model provides better results than local averages in dense enough networks
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