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Autor:
den Boef, J.B., Cornelisse, J., Groth, P., Alam, M., Buscaldi, D., Cochez, M., Osborne, F., Reforgiato Recupero, D., Sack, H.
Publikováno v:
Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG 2021): co-located with the 20th International Semantic Web Conference (ISWC 2021) : Virtual Conference, online, October 25, 2021
Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG 2021)
Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG 2021)
Exponential computational cost arises when graph convolutions are performed on large graphs such as knowledge graphs. This computational bottleneck, dubbed the ‘neighbor explosion’ problem, has been overcome through application of graph sampling
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::1bb16d1078129c611b83e2c68b6c93d3
https://dare.uva.nl/personal/pure/en/publications/graphpope-retaining-structural-graph-information-using-positionaware-node-embeddings(c856a3b1-a15d-4dd2-9e92-68bc4d99e0f1).html
https://dare.uva.nl/personal/pure/en/publications/graphpope-retaining-structural-graph-information-using-positionaware-node-embeddings(c856a3b1-a15d-4dd2-9e92-68bc4d99e0f1).html