Attention-based message passing and dynamic graph convolution for spatiotemporal data imputation.

ABSTRACT: Although numerous spatiotemporal approaches have been presented to address the problem of missing spatiotemporal data, there are still limitations in concurrently capturing the underlying spatiotemporal dependence of spatiotemporal graph data. Furthermore, most imputation methods miss the hidden dynamic connection associations that exist between graph nodes over time. To address the aforementioned spatiotemporal data imputation challenge, we present an attention-based message passing and dynamic graph convolution network (ADGCN). Specifically, this paper uses attention mechanisms to unify temporal and spatial continuity and aggregate node neighbor information in multiple directions. Furthermore, a dynamic graph convolution module is designed to capture constantly changing spatial correlati

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10140034 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature