  User attributes, such as gender and education, face severe incompleteness in
social networks. In order to make this kind of valuable data usable for
downstream tasks like user profiling and personalized recommendation, attribute
inference aims to infer users' missing attribute labels based on observed data.
Recently, variational autoencoder (VAE), an end-to-end deep generative model,
has shown promising performance by handling the problem in a semi-supervised
way. However, VAEs can easily suffer from over-fitting and over-smoothing when
applied to attribute inference. To be specific, VAE implemented with
multi-layer perceptron (MLP) can only reconstruct input data but fail in
inferring missing parts. While using the trending graph neural networks (GNNs)
as encoder has the problem that GNNs aggregate redundant information from
neighborhood and generate indistinguishable user representations, which is
known as over-smoothing. In this paper, we propose an attribute
\textbf{Infer}ence model based on \textbf{A}dversarial \textbf{VAE}
(Infer-AVAE) to cope with these issues. Specifically, to overcome
over-smoothing, Infer-AVAE unifies MLP and GNNs in encoder to learn positive
and negative latent representations respectively. Meanwhile, an adversarial
network is trained to distinguish the two representations and GNNs are trained
to aggregate less noise for more robust representations through adversarial
training. Finally, to relieve over-fitting, mutual information constraint is
introduced as a regularizer for decoder, so that it can make better use of
auxiliary information in representations and generate outputs not limited by
observations. We evaluate our model on 4 real-world social network datasets,
experimental results demonstrate that our model averagely outperforms baselines
by 7.0$\%$ in accuracy.
