Zobrazeno 1 - 10
of 115
pro vyhledávání: '"Aridhi, Sabeur"'
The task of inferring the missing links in a graph based on its current structure is referred to as link prediction. Link prediction methods that are based on pairwise node similarity are well-established approaches in the literature. They show good
Externí odkaz:
http://arxiv.org/abs/2008.08879
Autor:
Karabadji, Nour El Islem, Amara Korba, Abdelaziz, Assi, Ali, Seridi, Hassina, Aridhi, Sabeur, Dhifli, Wajdi
Publikováno v:
In Expert Systems With Applications 1 September 2023 225
Publikováno v:
In Future Generation Computer Systems September 2022 134:334-347
Publikováno v:
In Knowledge-Based Systems 17 August 2022 250
Autor:
Aridhi, Sabeur, Alborzi, Seyed Ziaeddin, Smaïl-Tabbone, Malika, Devignes, Marie-Dominique, Ritchie, David
Understanding protein function is one of the keys to understanding life at the molecular level. It is also important in several scenarios including human disease and drug discovery. In this age of rapid and affordable biological sequencing, the numbe
Externí odkaz:
http://arxiv.org/abs/1708.07074
Recently, distributed processing of large dynamic graphs has become very popular, especially in certain domains such as social network analysis, Web graph analysis and spatial network analysis. In this context, many distributed/parallel graph process
Externí odkaz:
http://arxiv.org/abs/1701.00546
Autor:
Veras, Marcelo B.A., Sarker, Bishnu, Aridhi, Sabeur, Gomes, João P.P., Macêdo, José A.F., Nguifo, Engelbert Mephu, Devignes, Marie-Dominique, Smaïl-Tabbone, Malika
Publikováno v:
In Knowledge-Based Systems 28 February 2022 238
We propose a scalable method for semi-supervised (transductive) learning from massive network-structured datasets. Our approach to semi-supervised learning is based on representing the underlying hypothesis as a graph signal with small total variatio
Externí odkaz:
http://arxiv.org/abs/1611.00714
Recently, increasingly large amounts of data are generated from a variety of sources. Existing data processing technologies are not suitable to cope with the huge amounts of generated data. Yet, many research works focus on Big Data, a buzzword refer
Externí odkaz:
http://arxiv.org/abs/1610.09962
Big graph mining is an important research area and it has attracted considerable attention. It allows to process, analyze, and extract meaningful information from large amounts of graph data. Big graph mining has been highly motivated not only by the
Externí odkaz:
http://arxiv.org/abs/1602.03072