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Review of heterogeneous graph embedding methods based on deep learning techniques and comparing their efficiency in node classification

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Abstract

Graph embedding is an advantageous technique for reducing computational costs and effectively using graph information in machine learning tasks like classification, clustering, and link prediction. As a result, it has become a key method in various research areas. However, different embedding methods may be used depending on the variety of graphs available. One of the most commonly used graph types is the heterogeneous graph (HG) or heterogeneous information network (HIN), which presents unique challenges for graph embedding approaches due to its diverse set of nodes and edges. Several methods have been proposed for heterogeneous graph embedding in recent years to overcome these challenges. This paper aims to review the latest techniques used for this purpose, divided into two main parts: the first part describes the fundamental concepts and obstacles in heterogeneous graph embedding, while the second part compares the most critical methods. Finally, the results are summarized, outlining the challenges and opportunities for future directions.

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Notes

  1. http://dl.acm.org/

  2. https://dblp.uni-trier.de

  3. https://www.aminer.cn

  4. https://www.imdb.com

  5. https://www.yelp.com/dataset

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

  7. http://movie.douban.com/

  8. https://www.ncbi.nlm.nih.gov/pubmed/

  9. https://www.last.fm

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

    Azad Noori & Mohammad Ali Balafar

  2. Department of Software Engineering, Faculty of Computer Engineering and Information Technology, Azarbaijan Shahid Madani University, Tabriz, Iran

    Asgarali Bouyer

  3. Department of Mathematics and Computing, Mount Royal University, Calgary, Canada

    Khosro Salmani

  4. Department of Software Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul, Turkey

    Asgarali Bouyer

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  1. Azad Noori
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  2. Mohammad Ali Balafar
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  3. Asgarali Bouyer
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  4. Khosro Salmani
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Contributions

A.N. and M.B. wrote the main manuscript text. A.N., A.B., and K.S. designed, implemented the experiments, performed the analysis, and designed the figures and tables. M.B. and A.B. were involved in planning and supervised the work. All authors discussed the results and commented on the manuscript.

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Correspondence to Mohammad Ali Balafar.

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Noori, A., Balafar, M.A., Bouyer, A. et al. Review of heterogeneous graph embedding methods based on deep learning techniques and comparing their efficiency in node classification. Soc. Netw. Anal. Min. 14, 17 (2024). https://doi.org/10.1007/s13278-023-01178-6

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