Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Hamida Seba"'
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-19 (2023)
Abstract Networks are essential for analyzing complex systems. However, their growing size necessitates backbone extraction techniques aimed at reducing their size while retaining critical features. In practice, selecting, implementing, and evaluatin
Externí odkaz:
https://doaj.org/article/675ba72b412e405197b1033bfe6a1545
Publikováno v:
Discrete Mathematics & Theoretical Computer Science, Vol vol. 25:3 special issue..., Iss Special issues (2024)
Inspired by the split decomposition of graphs and rank-width, we introduce the notion of $r$-splits. We focus on the family of $r$-splits of a graph of order $n$, and we prove that it forms a hypergraph with several properties. We prove that such hyp
Externí odkaz:
https://doaj.org/article/c34320a252c24d9697b5e8d4345e23ca
Autor:
Rémy Crassard, Wael Abu-Azizeh, Olivier Barge, Jacques Élie Brochier, Frank Preusser, Hamida Seba, Abd Errahmane Kiouche, Emmanuelle Régagnon, Juan Antonio Sánchez Priego, Thamer Almalki, Mohammad Tarawneh
Publikováno v:
PLoS ONE, Vol 18, Iss 5, p e0277927 (2023)
Data on how Stone Age communities conceived domestic and utilitarian structures are limited to a few examples of schematic and non-accurate representations of various-sized built spaces. Here, we report the exceptional discovery of the up-to-now olde
Externí odkaz:
https://doaj.org/article/4961160c93fd409383e7957f3df4187f
Publikováno v:
Wireless Personal Communications. 130:2013-2038
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1c43078d08b98787f02371206bdee5d6
https://doi.org/10.1007/s11277-023-10369-5
https://doi.org/10.1007/s11277-023-10369-5
Publikováno v:
Neural Computing and Applications. 35:7035-7048
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::694eaa20b662773aaa356cbf56443397
https://doi.org/10.1007/s00521-022-08076-6
https://doi.org/10.1007/s00521-022-08076-6
Identity2Vec: learning mesoscopic structural identity representations via Poisson probability metric
Publikováno v:
International Journal of Data Science and Analytics.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f5599969a864d41affc0c8ed0357f5df
https://doi.org/10.1007/s41060-023-00390-z
https://doi.org/10.1007/s41060-023-00390-z
Graph Convolutional Networks (GCNs) are a subcategory of neural networks that can take a graph as input. They have been solicited lately due to their success and the ubiquity of data that can be represented as graphs. However, handling large graphs h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5c1c5d856eb56c96c6fe682883337acc
https://doi.org/10.21203/rs.3.rs-2648725/v1
https://doi.org/10.21203/rs.3.rs-2648725/v1
Publikováno v:
Complex Networks and Their Applications XI ISBN: 9783031211300
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::af8065f9e6db4fc8f3ba0a44fffc905c
https://doi.org/10.1007/978-3-031-21131-7_43
https://doi.org/10.1007/978-3-031-21131-7_43
Publikováno v:
Pattern Recognition Letters
Pattern Recognition Letters, Elsevier, 2021, 152, pp.107-114. ⟨10.1016/j.patrec.2021.09.019⟩
Pattern Recognition Letters, Elsevier, 2021, 152, pp.107-114. ⟨10.1016/j.patrec.2021.09.019⟩
This paper presents an effective dissimilarity measure for geometric graphs representing shapes. The proposed dissimilarity measure is a distance that combines a sparsification of the geometric graph based on the maximum diversity problem and a new n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c506558fd6470e7fc3507d0ae34f9e61
https://doi.org/10.1016/j.patrec.2021.09.019
https://doi.org/10.1016/j.patrec.2021.09.019