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Multi-temporal heterogeneous graph learning with pattern-aware attention for industrial chain risk detection

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Abstract

Analyzing multi-channel data related to the industrial chain through graph representation learning is of significant value for industrial chain risk detection. Multi-channel data related to the industrial chain exhibits strong correlations, which enables such data to be modeled using graph structures. Furthermore, due to multi-channel data being multi-source, heterogeneous, and containing temporal information, industrial chain risk detection can be effectively conducted from the perspective of temporal heterogeneous graph representation learning. However, existing methods primarily obtain node features by sequentially processing the temporal heterogeneous information within a single graph, and they use attention based on discrete tokens to learn long-term information, which overlooks the multi-temporal information interactions between nodes and fails to effectively capture the temporal patterns in long-term information. To address these problems, we propose a multi-temporal heterogeneous graph learning with pattern-aware attention (MTHG-PA) for industrial chain risk detection. Firstly, we propose a method for interactive aggregation of multi-temporal heterogeneous information to learn node features, thereby avoiding the problem of insufficient information interaction between nodes across time. Secondly, we design a temporal pattern-aware attention module to learn the temporal patterns in long-term information, addressing the problem that traditional attention mechanisms can only handle discrete tokens. We conduct extensive experiments on three real-world datasets. The results show that our proposed method achieves superior performance in temporal heterogeneous graph learning compared to state-of-the-art methods.

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Data Availability

No datasets were generated or analysed during the current study.

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Acknowledgements

This work is supported by the National Key R &D Program of China under Grant No. 2022YFB3304300; and the NSFC under Grant No. U23A20297 and 62072087; and the Guangdong Basic and Applied Basic Research Foundation under Grant No. 2023A1515110268; and Postdoctoral Research Foundation of Northeastern University under Grant No. 20230303.

Funding

The authors would like to acknowledge the support provided by the National Key R &D Program of China under Grant No. 2022YFB3304300; and the NSFC under Grant No. U23A20297; and the Guangdong Basic and Applied Basic Research Foundation under Grant No. 2023A1515110268; and Postdoctoral Research Foundation of Northeastern University under Grant No. 20230303.

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Authors and Affiliations

  1. College of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China

    Ziheng Li, Yongjiao Sun, Xin Bi & Hangxu Ji

  2. School of Information and Software Engineering, University of Electronic Science and Technology, Chengdu, 610054, China

    Ruijin Wang

  3. School of Computing, Wuhan University, Wuhan, 430072, China

    Shi Ying

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  1. Ziheng Li
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  2. Yongjiao Sun
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  3. Xin Bi
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Contributions

Y. Sun and X. Bi provided the conceptual design of the study, Z. Li wrote the manuscript and completed the experiments, R. Wang, S. Ying, and H. Ji provided supervision for the paper. All the authors reviewed the manuscript.

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Correspondence to Yongjiao Sun.

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Li, Z., Sun, Y., Bi, X. et al. Multi-temporal heterogeneous graph learning with pattern-aware attention for industrial chain risk detection. World Wide Web 27, 38 (2024). https://doi.org/10.1007/s11280-024-01280-5

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