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摘要
For the reconstruction of large-scale network data, a Distributed Batch Reconstruction algorithm via Sobolev Smoothness on Cartesian product graph (DBR-SSC) is proposed, which is based on the Graph Signal Processing (GSP) theory. In the proposed algorithm, the time-varying graph signal is firstly divided into multiple signal segments in time dimension, and a product graph is constructed from graphs at each time instant via Cartesian product. Secondly, the reconstruction of the time-varying graph signal in each segment is formulated as an optimization problem by exploiting the Sobolev difference smoothness on the Cartesian product graph. Finally, a distributed algorithm with high convergence rate is devised to solve the optimization problem. Two real world data sets are used for experiments, and it is shown that the proposed algorithm has low reconstruction error and high convergence rate. ? 2023 Science Press.
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