Zobrazeno 1 - 3
of 3
pro vyhledávání: '"de Almeida, Filipe Miguel Goncalves"'
Autor:
Ferludin, Oleksandr, Eigenwillig, Arno, Blais, Martin, Zelle, Dustin, Pfeifer, Jan, Sanchez-Gonzalez, Alvaro, Li, Wai Lok Sibon, Abu-El-Haija, Sami, Battaglia, Peter, Bulut, Neslihan, Halcrow, Jonathan, de Almeida, Filipe Miguel Gonçalves, Gonnet, Pedro, Jiang, Liangze, Kothari, Parth, Lattanzi, Silvio, Linhares, André, Mayer, Brandon, Mirrokni, Vahab, Palowitch, John, Paradkar, Mihir, She, Jennifer, Tsitsulin, Anton, Villela, Kevin, Wang, Lisa, Wong, David, Perozzi, Bryan
TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. It is designed from the bottom up to support the kinds of rich heterogeneous graph data that occurs in today's information ecosystems. In addition to enabling mach
Externí odkaz:
http://arxiv.org/abs/2207.03522
Autor:
Wu, Yuchen, Bateni, MohammadHossein, Linhares, Andre, de Almeida, Filipe Miguel Goncalves, Montanari, Andrea, Norouzi-Fard, Ashkan, Tardos, Jakab
The community detection problem requires to cluster the nodes of a network into a small number of well-connected "communities". There has been substantial recent progress in characterizing the fundamental statistical limits of community detection und
Externí odkaz:
http://arxiv.org/abs/2106.04805
Autor:
Postăvaru, Ştefan, Tsitsulin, Anton, de Almeida, Filipe Miguel Gonçalves, Tian, Yingtao, Lattanzi, Silvio, Perozzi, Bryan
In this paper, we introduce InstantEmbedding, an efficient method for generating single-node representations using local PageRank computations. We theoretically prove that our approach produces globally consistent representations in sublinear time. W
Externí odkaz:
http://arxiv.org/abs/2010.06992