  Machine learning with missing data has been approached in two different ways,
including feature imputation where missing feature values are estimated based
on observed values, and label prediction where downstream labels are learned
directly from incomplete data. However, existing imputation models tend to have
strong prior assumptions and cannot learn from downstream tasks, while models
targeting label prediction often involve heuristics and can encounter
scalability issues. Here we propose GRAPE, a graph-based framework for feature
imputation as well as label prediction. GRAPE tackles the missing data problem
using a graph representation, where the observations and features are viewed as
two types of nodes in a bipartite graph, and the observed feature values as
edges. Under the GRAPE framework, the feature imputation is formulated as an
edge-level prediction task and the label prediction as a node-level prediction
task. These tasks are then solved with Graph Neural Networks. Experimental
results on nine benchmark datasets show that GRAPE yields 20% lower mean
absolute error for imputation tasks and 10% lower for label prediction tasks,
compared with existing state-of-the-art methods.
