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

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dc.authorwosidBouyer, Asgarali/JOZ-6483-2023
dc.contributor.authorNoori, Azad
dc.contributor.authorBalafar, Mohammad Ali
dc.contributor.authorBouyer, Asgarali
dc.contributor.authorSalmani, Khosro
dc.date.accessioned2024-05-19T14:42:23Z
dc.date.available2024-05-19T14:42:23Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractGraph 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.en_US
dc.identifier.doi10.1007/s13278-023-01178-6
dc.identifier.issn1869-5450
dc.identifier.issn1869-5469
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85181244183en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1007/s13278-023-01178-6
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5235
dc.identifier.volume14en_US
dc.identifier.wosWOS:001133047300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Wienen_US
dc.relation.ispartofSocial Network Analysis and Miningen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectHeterogeneous Graph Embeddingen_US
dc.subjectDeep Learningen_US
dc.subjectNode Classificationen_US
dc.subjectHeterogeneous Networken_US
dc.subjectHeterogeneous Graph Transformeren_US
dc.titleReview of heterogeneous graph embedding methods based on deep learning techniques and comparing their efficiency in node classificationen_US
dc.typeReview Articleen_US

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