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MHGNN: Multi-view fusion based Heterogeneous Graph Neural Network

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Abstract

Heterogeneous Graph (HG) is a data structure composed of various types of nodes and rich relational information, which can accurately show complex application scenarios in the real world. Although heterogeneous graph neural networks (HGNNs) have been widely applied to model HGs, there are still some issues that need to be addressed. On the one hand, most of HGNNs ignore the fine-grained information when modeling HGs, such as attribute and topology overcoupling due to the accumulation of multi-source heterogeneous information in message passing. On the other hand, HGNNs are designed from a single view (based on metapath or relation awareness), which undoubtedly leads to information loss and makes it difficult to fully extract potential interactions in HGs. To tackle the aforementioned limitations, a Multi-view fusion based Heterogeneous Graph Neural Network (MHGNN) is proposed, which is modeled from node view, network schema view, and semantics view to mine the information from different granularity in HGs. MHGNN extracts the fine-gained information of nodes, heterogeneous interaction of neighboring nodes, and mutual influence between different semantics from three views respectively. Then, the model integrates these information as the final node representation. To prove the effectiveness of this work, extensive experiments are conducted on four real-world datasets, and comparisons are made with seven competitive baselines. The results demonstrate that the proposed MHGNN significantly outperforms state-of-the-art methods. Source codes are available at https://github.com/ZZY-GraphMiningLab/MHGNN.

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Availability of supporting data

The datasets used in the experiments are publicly available in the online repository.

Notes

  1. https://github.com/Andy-Border/NSHE

  2. https://github.com/cynricfu/MAGNN

  3. https://github.com/cynricfu/MAGNN

  4. https://github.com/AndyJZhao/HGSL/tree/main/data

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Acknowledgements

This work is supported by National Key R &D Program of China(Grant No.2022ZD0119501); the National Natural Science Foundation of China (Grant No. 62072288, 52374221, 62302277), the Natural Science Foundation of Shandong Province (Grant No. ZR2022MF268, ZR2022QF136, ZR2021QG038), the Taishan Scholar Program of Shandong Province(Grant No.tsqn202211154, ts20190936), the Natural Science Foundation of Shandong Province (Youth Program, Grant No.ZR2022QF136).

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Authors and Affiliations

  1. College of electronic and information Engineering, Shandong University of Science and Technology, Qianwangang Road, Qingdao, 266590, Shandong, China

    Chao Li, **angkai Zhu & Qingtian Zeng

  2. Institute of Information Science, Bei**g Jiaotong University, Haidian District, 100044, Bei**g, China

    Yeyu Yan

  3. College of Computer Science and Engineering, Shandong University of Science and Technology, Qianwangang Road, Qingdao, 266590, Shandong, China

    Zhongying Zhao, Lingtao Su & Qingtian Zeng

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Contributions

Chao Li, **angkai Zhu, Yeyu Yan, Zhongying Zhao, Lingtao Su, Qingtian Zeng wrote the main manuscript text; **angkai Zhu and Yeyu Yan prepared the result of our experiments; All authors reviewed the manuscript.

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Correspondence to Chao Li or Lingtao Su.

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Li, C., Zhu, X., Yan, Y. et al. MHGNN: Multi-view fusion based Heterogeneous Graph Neural Network. Appl Intell (2024). https://doi.org/10.1007/s10489-024-05567-y

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