  Recent works for attributed network clustering utilize graph convolution to
obtain node embeddings and simultaneously perform clustering assignments on the
embedding space. It is effective since graph convolution combines the
structural and attributive information for node embedding learning. However, a
major limitation of such works is that the graph convolution only incorporates
the attribute information from the local neighborhood of nodes but fails to
exploit the mutual affinities between nodes and attributes. In this regard, we
propose a variational co-embedding learning model for attributed network
clustering (VCLANC). VCLANC is composed of dual variational auto-encoders to
simultaneously embed nodes and attributes. Relying on this, the mutual affinity
information between nodes and attributes could be reconstructed from the
embedding space and served as extra self-supervised knowledge for
representation learning. At the same time, trainable Gaussian mixture model is
used as priors to infer the node clustering assignments. To strengthen the
performance of the inferred clusters, we use a mutual distance loss on the
centers of the Gaussian priors and a clustering assignment hardening loss on
the node embeddings. Experimental results on four real-world attributed network
datasets demonstrate the effectiveness of the proposed VCLANC for attributed
network clustering.
