Titlebook: Dynamic Network Representation Based on Latent Factorization of Tensors; Hao Wu,Xuke Wu,Xin Luo Book 2023 The Editor(s) (if applicable) an

寄生蟲

殺人

巨大沒有

K,ter vision and other fields [1–5]. For a third-order HDI tensor modeling a dynamic network, this book carry out some preliminary research on latent factorization of tensors methods to implement accurate representation for dynamic networks. Further, in real industrial applications, in order to tackle

Allure

extrovert

https://doi.org/10.1007/978-981-19-8934-6Dynamic network representation; Latent factorization of tensors; High-dimensional and incomplete tenso

ENACT

978-981-19-8933-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor

軍械庫

Hao Wu,Xuke Wu,Xin LuoExposes readers to a novel research perspective regarding dynamic network representation.Presents four dynamic network representation methods based on latent factorization of tensors.Accomplishes accu

Hay-Fever

SpringerBriefs in Computer Sciencehttp://image.papertrans.cn/e/image/283681.jpg

圓柱

antedate

Multiple Biases-Incorporated Latent Factorization of Tensors,tion on extracting useful knowledge form an HDI tensor. However, existing LFT-based models lack solid consideration for the volatility of dynamic network data, thereby leading to the descent of model representation learning ability. To tackle this problem, this chapter proposes a multiple biases-inc