Graph Representation Learning and Its Applications: A Survey
Rattachement africain : kr. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Graphs are data structures that effectively represent relational data in the real world. Graph representation learning is a significant task since it could facilitate various downstream tasks, such as node classification, link prediction, etc. Graph representation learning aims to map graph entities to low-dimensional vectors while preserving graph structure and entity relationships. Over the decades, many models have been proposed for graph representation learning. This paper aims to show a comprehensive picture of graph representation learning models, including traditional and state-of-the-art models on various graphs in different geometric spaces. First, we begin with five types of graph embedding models: graph kernels, matrix factorization models, shallow models, deep-learning models, and non-Euclidean models. In addition, we also discuss graph transformer models and Gaussian embedding models. Second, we present practical applications of graph embedding models, from constructing graphs for specific domains to applying models to solve tasks. Finally, we discuss challenges for existing models and future research directions in detail. As a result, this paper provides a structured overview of the diversity of graph embedding models.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Graph Representation Learning and Its Applications: A Survey
- Date Crossref
- 21/04/2023
- Éditeur
- MDPI AG
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Catholic University of Korea Department of Artificial Intelligence pays non établi dans la noticeUniversité ou école supérieure
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Korea Institute of Atmospheric Prediction Systems Data Assimilation Group pays non établi dans la noticeOrganisation à but non lucratif
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Dongguk University Department of Social Welfare pays non établi dans la noticeUniversité ou école supérieure
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Seoul National University Semiconductor Devices and Circuits Laboratory pays non établi dans la noticeUniversité ou école supérieure
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Advanced Institute of Convergence Technology pays non établi dans la noticeStructure de recherche
Department of Artificial Intelligence — Catholic University of Korea, Data Assimilation Group — Korea Institute of Atmospheric Prediction Systems et Department of Social Welfare — Dongguk University, avec 2 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.