Data imputation is a prevalent and important task due to the ubiquitousness
of missing data. Many efforts try to first draft a completed data and second
refine to derive the imputation results, or "draft-then-refine" for short. In
this work, we analyze this widespread practice from the perspective of
Dirichlet energy. We find that a rudimentary "draft" imputation will decrease
the Dirichlet energy, thus an energy-maintenance "refine" step is in need to
recover the overall energy. Since existing "refine" methods such as Graph
Convolutional Network (GCN) tend to cause further energy decline, in this work,
we propose a novel framework called Graph Laplacian Pyramid Network (GLPN) to
preserve Dirichlet energy and improve imputation performance. GLPN consists of
a U-shaped autoencoder and residual networks to capture global and local
detailed information respectively. By extensive experiments on several
real-world datasets, GLPN shows superior performance over state-of-the-art
methods under three different missing mechanisms. Our source code is available
at https://github.com/liguanlue/GLPN.Comment: 12 pages, 5 figure