DANE: Domain Adaptive Network Embedding
This addresses the domain adaptation problem for graph-structured data, enabling downstream models to transfer across different networks, though it is incremental as it builds on existing network embedding and domain adaptation techniques.
The paper tackles the problem of learning transferable network embeddings across multiple graphs, proposing DANE, a domain adaptive framework that uses graph convolutional networks and adversarial learning to align embedding distributions, which outperforms state-of-the-art baselines in cross-network tasks.
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.