Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Zekarias T. Kefato"'
Publikováno v:
Companion Proceedings of the ACM Web Conference 2023.
Link prediction between two nodes is a critical task in graph ma- chine learning. Most approaches are based on variants of graph neural networks (GNNs) that focus on transductive link prediction and have high inference latency. However, many real-wor
Publikováno v:
Companion Proceedings of the Web Conference 2022
WWW '22: Companion Proceedings of the Web Conference 2022
WWW '22: Companion Proceedings of the Web Conference 2022
Heterogeneous Information Network (HIN) embedding has been a prevalent approach to learn representations off semantically-rich heterogeneous networks. Most HIN embedding methods exploit meta-paths to retain high-order structures, yet, their performan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e6a78d322d7c2912a4c1a9efeeef4f40
https://zenodo.org/record/6384991
https://zenodo.org/record/6384991
Publikováno v:
WWW
In recommender systems (RSs), predicting the next item that a user interacts with is critical for user retention. While the last decade has seen an explosion of RSs aimed at identifying relevant items that match user preferences, there is still a ran
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aef9e08b64bfac7299e431e1f319cfde
http://arxiv.org/abs/2011.05208
http://arxiv.org/abs/2011.05208
Publikováno v:
Computing. 101:187-209
Network representation learning (NRL) enables the application of machine learning tasks such as classification, prediction and recommendation to networks. Apart from their graph structure, networks are often associated with diverse information in the
Publikováno v:
Pattern Recognition. 121:108252
Predicting the future trajectories of pedestrians is a challenging problem that has a range of application, from crowd surveillance to autonomous driving. In literature, methods to approach pedestrian trajectory prediction have evolved, transitioning
Publikováno v:
Machine Learning, Optimization, and Data Science ISBN: 9783030137083
LOD
LOD
Network representation learning has recently attracted considerable interest, because of its effectiveness in performing important network analysis tasks such as link prediction and node classification. However, most of the existing studies rely on t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5c6ef21442b3c0283767f02c71019062
https://doi.org/10.1007/978-3-030-13709-0_12
https://doi.org/10.1007/978-3-030-13709-0_12
Autor:
Sarunas Girdzijauskas, Alberto Montresor, Leila Bahri, Zekarias T. Kefato, Amira Soliman, Nasrullah Sheikh
Publikováno v:
SNAMS
Effectively predicting whether a given post or tweet is going to become viral in online social networks is of paramount importance for several applications, such as trend and break-out forecasting. While several attempts towards this end exist, most
Publikováno v:
SNAMS
Network Representation Learning (NRL) is a method to learn a representation of a graph in a low-dimensional space, such that the representation can be later utilized easily in various machine learning tasks such as classification, recommendation, and
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783319729251
MOD
MOD
Network representation learning (NRL) is a task of learning an embedding of nodes in a low-dimensional space. Recent advances in this area have achieved interesting results; however, as there is no solution that fits all kind of networks, NRL algorit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c790f7e97fe6115c4f8736d6d12f6f79
https://doi.org/10.1007/978-3-319-72926-8_24
https://doi.org/10.1007/978-3-319-72926-8_24