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GNNs have recently become popular because of their ability to obtain a contextual representation of each node taking into account information from its surroundings. However, existing work has focused on the development of GNN architectures, using basic domain-specific information about the nodes to compute embeddings. Meanwhile, in the closely-related area of knowledge graphs, much effort has been put towards developing deep learning techniques to obtain node embeddings that preserve information about relationships and structure without relying on domain-specific data. The potential application of deep embeddings of knowledge graphs in GNNs remains largely unexplored. In this paper, we carry out a number of experiments to answer open research questions about the impact on GNNs performance when combined with deep embeddings. We test 7 different deep embeddings across several attribute prediction tasks in two state-of-art attribute-rich datasets. We conclude that, while there is a significant performance improvement, its magnitude varies heavily depending on the specific task and deep embedding technique considered.<\/jats:p>","DOI":"10.1007\/s10489-023-04685-3","type":"journal-article","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T03:30:57Z","timestamp":1687923057000},"page":"22415-22428","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep embeddings and Graph Neural Networks: using context to improve domain-independent predictions"],"prefix":"10.1007","volume":"53","author":[{"given":"Fernando","family":"Sola","sequence":"first","affiliation":[]},{"given":"Daniel","family":"Ayala","sequence":"additional","affiliation":[]},{"given":"Inma","family":"Hern\u00e1ndez","sequence":"additional","affiliation":[]},{"given":"David","family":"Ruiz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,28]]},"reference":[{"key":"4685_CR1","doi-asserted-by":"publisher","unstructured":"Abadal S, Jain A, Guirado R, et\u00a0al (2022) Computing graph neural networks: A survey from algorithms to accelerators. 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