  Attributed network embedding has attracted plenty of interest in recent
years. It aims to learn task-independent, low-dimensional, and continuous
vectors for nodes preserving both topology and attribute information. Most of
the existing methods, such as random-walk based methods and GCNs, mainly focus
on the local information, i.e., the attributes of the neighbours. Thus, they
have been well studied for assortative networks (i.e., networks with
communities) but ignored disassortative networks (i.e., networks with
multipartite, hubs, and hybrid structures), which are common in the real world.
To enable model both assortative and disassortative networks, we propose a
block-based generative model for attributed network embedding from a
probability perspective. Specifically, the nodes are assigned to several blocks
wherein the nodes in the same block share the similar linkage patterns. These
patterns can define assortative networks containing communities or
disassortative networks with the multipartite, hub, or any hybrid structures.
To preserve the attribute information, we assume that each node has a hidden
embedding related to its assigned block. We use a neural network to
characterize the nonlinearity between node embeddings and node attributes. We
perform extensive experiments on real-world and synthetic attributed networks.
The results show that our proposed method consistently outperforms
state-of-the-art embedding methods for both clustering and classification
tasks, especially on disassortative networks.
