  Network embedding aims to find a way to encode network by learning an
embedding vector for each node in the network. The network often has property
information which is highly informative with respect to the node's position and
role in the network. Most network embedding methods fail to utilize this
information during network representation learning. In this paper, we propose a
novel framework, FANE, to integrate structure and property information in the
network embedding process. In FANE, we design a network to unify heterogeneity
of the two information sources, and define a new random walking strategy to
leverage property information and make the two information compensate. FANE is
conceptually simple and empirically powerful. It improves over the
state-of-the-art methods on Cora dataset classification task by over 5%, more
than 10% on WebKB dataset classification task. Experiments also show that the
results improve more than the state-of-the-art methods as increasing training
size. Moreover, qualitative visualization show that our framework is helpful in
network property information exploration. In all, we present a new way for
efficiently learning state-of-the-art task-independent representations in
complex attributed networks. The source code and datasets of this paper can be
obtained from https://github.com/GraphWorld/FANE.
