  Attributed networks are ubiquitous since a network often comes with auxiliary
attribute information e.g. a social network with user profiles. Attributed
Network Embedding (ANE) has recently attracted considerable attention, which
aims to learn unified low dimensional node embeddings while preserving both
structural and attribute information. The resulting node embeddings can then
facilitate various network downstream tasks e.g. link prediction. Although
there are several ANE methods, most of them cannot deal with incomplete
attributed networks with missing links and/or missing node attributes, which
often occur in real-world scenarios. To address this issue, we propose a robust
ANE method, the general idea of which is to reconstruct a unified denser
network by fusing two sources of information for information enhancement, and
then employ a random walks based network embedding method for learning node
embeddings. The experiments of link prediction, node classification,
visualization, and parameter sensitivity analysis on six real-world datasets
validate the effectiveness of our method to incomplete attributed networks.
