  High quality spatiotemporal signal is vitally important for real application
scenarios like energy management, traffic planning and cyber security. Due to
the uncontrollable factors like abrupt sensors breakdown or communication
fault, the spatiotemporal signal collected by sensors is always incomplete. A
dynamic graph convolutional network (DGCN) is effective for processing
spatiotemporal signal recovery. However, it adopts a static GCN and a sequence
neural network to explore the spatial and temporal patterns, separately. Such a
separated two-step processing is loose spatiotemporal, thereby failing to
capture the complex inner spatiotemporal correlation. To address this issue,
this paper proposes a Compact-Dynamic Graph Convolutional Network (CDGCN) for
spatiotemporal signal recovery with the following two-fold ideas: a) leveraging
the tensor M-product to build a unified tensor graph convolution framework,
which considers both spatial and temporal patterns simultaneously; and b)
constructing a differential smoothness-based objective function to reduce the
noise interference in spatiotemporal signal, thereby further improve the
recovery accuracy. Experiments on real-world spatiotemporal datasets
demonstrate that the proposed CDGCN significantly outperforms the
state-of-the-art models in terms of recovery accuracy.
