Time-varying Graph Learning Under Structured Temporal Priors

• URL: http://arxiv.org/abs/2110.05018v1
• Date: Mon, 11 Oct 2021 06:11:08 GMT
• Title: Time-varying Graph Learning Under Structured Temporal Priors
• Authors: Xiang Zhang and Qiao Wang
• Abstract summary: This paper endeavors to learn time-varying graphs by using structured temporal priors. We propose a structure named emphtemporal graph to characterize the underlying real temporal relations.
• Score: 16.73465046419929
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper endeavors to learn time-varying graphs by using structured temporal priors that assume underlying relations between arbitrary two graphs in the graph sequence. Different from many existing chain structure based methods in which the priors like temporal homogeneity can only describe the variations of two consecutive graphs, we propose a structure named \emph{temporal graph} to characterize the underlying real temporal relations. Under this framework, the chain structure is actually a special case of our temporal graph. We further proposed Alternating Direction Method of Multipliers (ADMM), a distributed algorithm, to solve the induced optimization problem. Numerical experiments demonstrate the superiorities of our method.

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