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Activity Number: 131
Type: Contributed
Date/Time: Monday, August 1, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #321067
Title: Learning Network Dynamics via Regularized Tensor Decomposition
Author(s): Yun-Jhong Wu* and Elizaveta Levina and Ji Zhu
Companies: University of Michigan and University of Michigan and University of Michigan
Keywords: dynamic networks ; non-negative tensor decomposition ; under-complete tensor representation ; power method

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.

Authors who are presenting talks have a * after their name.

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