Heterogeneous Attributed Network Embedding with Graph Convolutional Networks

Authors

  • Yueyang Wang Zhejiang University
  • Ziheng Duan Zhejiang University
  • Binbing Liao Zhejiang University
  • Fei Wu Zhejiang University
  • Yueting Zhuang Zhejiang University

DOI:

https://doi.org/10.1609/aaai.v33i01.330110061

Abstract

Network embedding which assigns nodes in networks to lowdimensional representations has received increasing attention in recent years. However, most existing approaches, especially the spectral-based methods, only consider the attributes in homogeneous networks. They are weak for heterogeneous attributed networks that involve different node types as well as rich node attributes and are common in real-world scenarios. In this paper, we propose HANE, a novel network embedding method based on Graph Convolutional Networks, that leverages both the heterogeneity and the node attributes to generate high-quality embeddings. The experiments on the real-world dataset show the effectiveness of our method.

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Published

2019-07-17

How to Cite

Wang, Y., Duan, Z., Liao, B., Wu, F., & Zhuang, Y. (2019). Heterogeneous Attributed Network Embedding with Graph Convolutional Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 10061-10062. https://doi.org/10.1609/aaai.v33i01.330110061

Issue

Section

Student Abstract Track