Abstract:
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Real networks often evolve over time, and interactions between nodes in networks are usually observed only at certain specific time points. In this work, we consider network data with time-stamped links. We propose to model such a dynamic network using a low rank tensor representation. This model characterizes time trends of multiple rank-1 factors and can be used to approximate more complicate networks. We develop an approach to fit this model based on a tensor completion algorithm and a smoothness penalty in the time domain, implemented with a highly scalable power-iteration-based algorithm which can fit large sparse dynamic networks. The numerical experiments on simulated data as well as the Enron e-mail dataset demonstrate the potential of tensor methods for dynamic network data analysis.
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