Hypergraph Label Propagation Network


  • Yubo Zhang Tsinghua University
  • Nan Wang Tsinghua University
  • Yufeng Chen Tsinghua University
  • Changqing Zou Huawei
  • Hai Wan Tsinghua University
  • Xinbin Zhao Tsinghua University
  • Yue Gao Tsinghua University




In recent years, with the explosion of information on the Internet, there has been a large amount of data produced, and analyzing these data is useful and has been widely employed in real world applications. Since data labeling is costly, lots of research has focused on how to efficiently label data through semi-supervised learning. Among the methods, graph and hypergraph based label propagation algorithms have been a widely used method. However, traditional hypergraph learning methods may suffer from their high computational cost. In this paper, we propose a Hypergraph Label Propagation Network (HLPN) which combines hypergraph-based label propagation and deep neural networks in order to optimize the feature embedding for optimal hypergraph learning through an end-to-end architecture. The proposed method is more effective and also efficient for data labeling compared with traditional hypergraph learning methods. We verify the effectiveness of our proposed HLPN method on a real-world microblog dataset gathered from Sina Weibo. Experiments demonstrate that the proposed method can significantly outperform the state-of-the-art methods and alternative approaches.




How to Cite

Zhang, Y., Wang, N., Chen, Y., Zou, C., Wan, H., Zhao, X., & Gao, Y. (2020). Hypergraph Label Propagation Network. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 6885-6892. https://doi.org/10.1609/aaai.v34i04.6170



AAAI Technical Track: Machine Learning