Kavli Affiliate: Xiang Zhang
| First 5 Authors: Xiang Zhang, Qiao Wang, , ,
| Summary:
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.
| Search Query: ArXiv Query: search_query=au:”Xiang Zhang”&id_list=&start=0&max_results=10