Adversarial Deep Network Embedding for Cross-Network Node Classification


  • Xiao Shen The Hong Kong Polytechnic University
  • Quanyu Dai The Hong Kong Polytechnic University
  • Fu-lai Chung The Hong Kong Polytechnic University
  • Wei Lu University of Electronic Science and Technology of China
  • Kup-Sze Choi The Hong Kong Polytechnic University



In this paper, the task of cross-network node classification, which leverages the abundant labeled nodes from a source network to help classify unlabeled nodes in a target network, is studied. The existing domain adaptation algorithms generally fail to model the network structural information, and the current network embedding models mainly focus on single-network applications. Thus, both of them cannot be directly applied to solve the cross-network node classification problem. This motivates us to propose an adversarial cross-network deep network embedding (ACDNE) model to integrate adversarial domain adaptation with deep network embedding so as to learn network-invariant node representations that can also well preserve the network structural information. In ACDNE, the deep network embedding module utilizes two feature extractors to jointly preserve attributed affinity and topological proximities between nodes. In addition, a node classifier is incorporated to make node representations label-discriminative. Moreover, an adversarial domain adaptation technique is employed to make node representations network-invariant. Extensive experimental results demonstrate that the proposed ACDNE model achieves the state-of-the-art performance in cross-network node classification.




How to Cite

Shen, X., Dai, Q., Chung, F.- lai, Lu, W., & Choi, K.-S. (2020). Adversarial Deep Network Embedding for Cross-Network Node Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 34(03), 2991-2999.



AAAI Technical Track: Knowledge Representation and Reasoning