  Attributed network embedding aims to learn low-dimensional node
representations from both network structure and node attributes. Existing
methods can be categorized into two groups: (1) the first group learns two
separated node representations from network structure and node attribute
respectively and concatenates them together; (2) the other group obtains node
representations by translating node attributes into network structure or vice
versa. However, both groups have their drawbacks. The first group neglects the
correlation between network structure and node attributes, while the second
group assumes strong dependence between these two types of information. In this
paper, we address attributed network embedding from a novel perspective, i.e.,
learning node context representation for each node via modeling its attributed
local subgraph. To achieve this goal, we propose a novel attributed network
auto-encoder framework, namely ANAE. For a target node, ANAE first aggregates
the attribute information from its attributed local subgraph, obtaining its
low-dimensional representation. Next, ANAE diffuses the representation of the
target node to nodes in its local subgraph to reconstruct their attributes.
Such an encoder-decoder framework allows the learned representations to better
preserve the context information manifested in both network structure and node
attributes, thus having high capacity to learn good node representations for
attributed network. Extensive experimental results on real-world datasets
demonstrate that the proposed framework outperforms the state-of-the-art
approaches at the tasks of link prediction and node classification.
