  Given a graph with partial observations of node features, how can we estimate
the missing features accurately? Feature estimation is a crucial problem for
analyzing real-world graphs whose features are commonly missing during the data
collection process. Accurate estimation not only provides diverse information
of nodes but also supports the inference of graph neural networks that require
the full observation of node features. However, designing an effective approach
for estimating high-dimensional features is challenging, since it requires an
estimator to have large representation power, increasing the risk of
overfitting. In this work, we propose SVGA (Structured Variational Graph
Autoencoder), an accurate method for feature estimation. SVGA applies strong
regularization to the distribution of latent variables by structured
variational inference, which models the prior of variables as Gaussian Markov
random field based on the graph structure. As a result, SVGA combines the
advantages of probabilistic inference and graph neural networks, achieving
state-of-the-art performance in real datasets.
