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Heterogeneous graph neural networks analysis: a survey of techniques, evaluations and applications

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Abstract

Graph Neural Networks (GNNs) have achieved excellent performance of graph representation learning and attracted plenty of attentions in recent years. Most of GNNs aim to learn embedding vectors of the homogeneous graph which only contains single type of nodes and edges. However, the entities and their interactions in real world always have multiple types and naturally form the heterogeneous graph with rich structural and semantic information. As a result of this, it is beneficial to advance heterogeneous graph representation learning that can effectively promote the performance of complex network analysis. Existing survey papers of heterogeneous graph representation learning summarize all possible embedding techniques for graphs and make insufficient analysis for deep neural network models. To tackle this issue, in this paper, we systematically summarize and analyze existing heterogeneous graph neural networks (HGNNs) and categorize them based on their neural network architecture. Meanwhile, we collect commonly used heterogeneous graph datasets and summarize their statistical information. In addition, we compare the performances between HGNNs and shallow embedding models to show the powerful feature learning ability of HGNNs. Finally, we conclude the application scenarios of HGNNs and some possible future research directions. We hope that this paper can provide a useful framework for researchers who interested in HGNNs.

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Notes

  1. http://dblp.uni-trier.de.

  2. http://dl.acm.org/.

  3. https://grouplens.org/datasets/movielens/100k/.

  4. http://movie.douban.com/.

  5. http://www.yelp.com/datasetchallenge/.

  6. https://www.last.fm/.

  7. http://jmcauley.ucsd.edu/data/amazon.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 71774159 and 62272066, State Key Laboratory of NBC Protection for Civilian under Grant SKLNBC2020-23, and the Fundamental Research Funds for the Central Universities of China under Grant 2015XKMS085.

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, No. 1 Daxue Road, Xuzhou, 221116, Jiangsu, China

    Rui Bing, Guan Yuan & Fanrong Meng

  2. State Key Laboratory of NBC Protection for Civilian, Beijing, 100191, China

    Mu Zhu

  3. Digitization of Mine, Engineering Research Center of Ministry of Education, No. 1 Daxue Road, Xuzhou, 221116, Jiangsu, China

    Guan Yuan

  4. College of Computer Science and Engineering, Northwest Normal University, No. 967 Anning East Road, Lanzhou, 730070, Gansu, China

    Huifang Ma

  5. School of Software Engineering, Chengdu University of Information Technology, No. 24, Section 1, Xuefu Road, Chengdu, 610225, Sichuan, China

    Shaojie Qiao

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  1. Rui Bing
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Bing, R., Yuan, G., Zhu, M. et al. Heterogeneous graph neural networks analysis: a survey of techniques, evaluations and applications. Artif Intell Rev 56, 8003–8042 (2023). https://doi.org/10.1007/s10462-022-10375-2

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