  Network embedding is a highly effective method to learn low-dimensional node
vector representations with original network structures being well preserved.
However, existing network embedding algorithms are mostly developed for a
single network, which fail to learn generalized feature representations across
different networks. In this paper, we study a cross-network node classification
problem, which aims at leveraging the abundant labeled information from a
source network to help classify the unlabeled nodes in a target network. To
succeed in such a task, transferable features should be learned for nodes
across different networks. To this end, a novel cross-network deep network
embedding (CDNE) model is proposed to incorporate domain adaptation into deep
network embedding so as to learn label-discriminative and network-invariant
node vector representations. On one hand, CDNE leverages network structures to
capture the proximities between nodes within a network, by mapping more
strongly connected nodes to have more similar latent vector representations. On
the other hand, node attributes and labels are leveraged to capture the
proximities between nodes across different networks by making the same labeled
nodes across networks have aligned latent vector representations. Extensive
experiments have been conducted, demonstrating that the proposed CDNE model
significantly outperforms the state-of-the-art network embedding algorithms in
cross-network node classification.
