AS-GCN: Adaptive Semantic Architecture of Graph Convolutional Networks for Text-Rich Networks

Zhizhi Yu, Di Jin, Ziyang Liu, Dongxiao He, Xiao Wang, Hanghang Tong, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Graph Neural Networks (GNNs) have demonstrated great power in many network analytical tasks. However, graphs (i.e., networks) in the real world are usually text-rich, implying that valuable semantic information needs to be carefully considered. Existing GNNs for text-rich networks typically treat text as attribute words alone, which inevitably leads to the loss of important semantic structures, limiting the representation capability of GNNs. In this paper, we propose an end-to-end adaptive semantic architecture of graph convolutional networks, namely AS-GCN, which unifies neural topic model and graph convolutional networks, for text-rich network representation. Specifically, we utilize a neural topic model to extract the global topic semantics, and accordingly augment the original text-rich network into a tri-typed heterogeneous network, capturing both the local word-sequence semantic structure and the global topic semantic structure from text. We then design an effective semantic-aware propagation of information by introducing a discriminative convolution mechanism. We further propose two strategies, that is, distribution sharing and joint training, to adaptively generate a proper network structure based on the learning objective to improve network representation. Extensive experiments on text-rich networks illustrate that our new architecture outperforms the state-of-the-art methods by a significant improvement. Meanwhile, this architecture can also be applied to e-commerce search scenes, and experiments on a real e-commerce problem from JD further demonstrate the superiority of the proposed architecture over the baselines.

Original languageEnglish (US)
Title of host publicationProceedings - 21st IEEE International Conference on Data Mining, ICDM 2021
EditorsJames Bailey, Pauli Miettinen, Yun Sing Koh, Dacheng Tao, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781665423984
StatePublished - 2021
Event21st IEEE International Conference on Data Mining, ICDM 2021 - Virtual, Online, New Zealand
Duration: Dec 7 2021Dec 10 2021

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Conference21st IEEE International Conference on Data Mining, ICDM 2021
Country/TerritoryNew Zealand
CityVirtual, Online


  • adaptive semantic architecture
  • graph neural networks
  • text-rich networks

ASJC Scopus subject areas

  • Engineering(all)


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