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Embedding text-rich graph neural networks with sequence and topical semantic structures

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Abstract

Graph neural networks (GNNs) have demonstrated great power in tackling various analytical tasks on graph (i.e. network) data. However, graphs in the real world are usually text-rich, implying that valuable semantic structures need to be considered carefully. Existing GNNs for text-rich networks typically treat the text as attribute words alone, which inevitably leads to the loss of important semantic structures, limiting the representation capability of GNNs. To solve this limitation, we propose AS-GNN, an end-to-end adaptive GNN architecture via unified modelling of semantic structure and network propagation on text-rich networks. Specifically, we utilize semantic structure modelling part to capture both the local word-sequence and the global topic semantic structures from text. We then augment the original text-rich network into a tri-typed heterogeneous network (including document nodes, word nodes, and topic nodes) and accordingly design a semantic-aware propagation of information by introducing a discriminative convolutional mechanism. We further train these two parts together by leveraging distribution sharing and joint training strategies, so as to adaptively generate an appropriate network structure aiming at the learning objectives. In addition, we present a simplified semantic architecture S-GNN, which adopts the cascaded “Structure-GNN” pattern, to promote the efficiency of the model and be easily combined with existing GNNs. Extensive experiments on text-rich networks demonstrate the superiority of our new architectures over state of the arts. Meanwhile, such architectures can also be applied to e-commerce search scenes, and experiments on a real e-commerce problem from JD further illustrate the effectiveness of AS-GNN over the baselines.

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Notes

  1. http://zhang18f.myweb.cs.uwindsor.ca/datasets/.

  2. https://www.cs.cornell.edu/projects/kddcup/datasets.html.

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Acknowledgements

This work was supported by the Natural Science Foundation of China under Grants 62272340, 62276187, 61876128, and 62172052.

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Author notes

  1. Work is done during Ziyang Liu work at JD.com.

Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China

    Zhizhi Yu, Di Jin & Dongxiao He

  2. School of Software, Tsinghua University, Beijing, 100084, China

    Ziyang Liu

  3. School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Xiao Wang

  4. Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, 61801, USA

    Hanghang Tong & Jiawei Han

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  1. Zhizhi Yu
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Correspondence to Dongxiao He.

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Yu, Z., Jin, D., Liu, Z. et al. Embedding text-rich graph neural networks with sequence and topical semantic structures. Knowl Inf Syst 65, 613–640 (2023). https://doi.org/10.1007/s10115-022-01768-4

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  • DOI: https://doi.org/10.1007/s10115-022-01768-4

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