  Facing the sparsity of user attributes on social networks, attribute
inference aims at inferring missing attributes based on existing data and
additional information such as social connections between users. Recently,
Variational Autoencoders (VAEs) have been successfully applied to solve the
problem in a semi-supervised way. However, the latent representations learned
by the encoder contain either insufficient or useless information: i) MLPs can
successfully reconstruct the input data but fail in completing missing part,
ii) GNNs merge information according to social connections but suffer from
over-smoothing, which is a common problem with GNNs. Moreover, existing methods
neglect regulating the decoder, as a result, it lacks adequate inference
ability and faces severe overfitting. To address the above issues, we propose
an attribute inference model based on adversarial VAE (Infer-AVAE). Our model
deliberately unifies MLPs and GNNs in encoder to learn dual latent
representations: one contains only the observed attributes of each user, the
other converges extra information from the neighborhood. Then, an adversarial
network is trained to leverage the differences between the two representations
and adversarial training is conducted to guide GNNs using MLPs for robust
representations. What's more, mutual information constraint is introduced in
loss function to specifically train the decoder as a discriminator. Thus, it
can make better use of auxiliary information in the representations for
attribute inference. Based on real-world social network datasets, experimental
results demonstrate that our model averagely outperforms state-of-art by 7.0%
in accuracy.
