Abstract
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 well-recognized network embedding baselines in cross-network domain adaptation
tasks