  Effective data imputation demands rich latent ``structure" discovery
capabilities from ``plain" tabular data. Recent advances in graph neural
networks-based data imputation solutions show their strong structure learning
potential by directly translating tabular data as bipartite graphs. However,
due to a lack of relations between samples, those solutions treat all samples
equally which is against one important observation: ``similar sample should
give more information about missing values." This paper presents a novel
Iterative graph Generation and Reconstruction framework for Missing data
imputation(IGRM). Instead of treating all samples equally, we introduce the
concept: ``friend networks" to represent different relations among samples. To
generate an accurate friend network with missing data, an end-to-end friend
network reconstruction solution is designed to allow for continuous friend
network optimization during imputation learning. The representation of the
optimized friend network, in turn, is used to further optimize the data
imputation process with differentiated message passing. Experiment results on
eight benchmark datasets show that IGRM yields 39.13% lower mean absolute error
compared with nine baselines and 9.04% lower than the second-best. Our code is
available at https://github.com/G-AILab/IGRM.
