  Recent works reveal that network embedding techniques enable many machine
learning models to handle diverse downstream tasks on graph structured data.
However, as previous methods usually focus on learning embeddings for a single
network, they can not learn representations transferable on multiple networks.
Hence, it is important to design a network embedding algorithm that supports
downstream model transferring on different networks, known as domain
adaptation. In this paper, we propose a novel Domain Adaptive Network Embedding
framework, which applies graph convolutional network to learn transferable
embeddings. In DANE, nodes from multiple networks are encoded to vectors via a
shared set of learnable parameters so that the vectors share an aligned
embedding space. The distribution of embeddings on different networks are
further aligned by adversarial learning regularization. In addition, DANE's
advantage in learning transferable network embedding can be guaranteed
theoretically. Extensive experiments reflect that the proposed framework
outperforms other state-of-the-art network embedding baselines in cross-network
domain adaptation tasks.

Machine Learning;Social and Information Networks;Machine Learning