標(biāo)題: Titlebook: Dynamic Network Representation Based on Latent Factorization of Tensors; Hao Wu,Xuke Wu,Xin Luo Book 2023 The Editor(s) (if applicable) an [打印本頁(yè)] 作者: Disaster 時(shí)間: 2025-3-21 19:42
書(shū)目名稱(chēng)Dynamic Network Representation Based on Latent Factorization of Tensors影響因子(影響力)
書(shū)目名稱(chēng)Dynamic Network Representation Based on Latent Factorization of Tensors影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Dynamic Network Representation Based on Latent Factorization of Tensors網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Dynamic Network Representation Based on Latent Factorization of Tensors網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Dynamic Network Representation Based on Latent Factorization of Tensors被引頻次
書(shū)目名稱(chēng)Dynamic Network Representation Based on Latent Factorization of Tensors被引頻次學(xué)科排名
書(shū)目名稱(chēng)Dynamic Network Representation Based on Latent Factorization of Tensors年度引用
書(shū)目名稱(chēng)Dynamic Network Representation Based on Latent Factorization of Tensors年度引用學(xué)科排名
書(shū)目名稱(chēng)Dynamic Network Representation Based on Latent Factorization of Tensors讀者反饋
書(shū)目名稱(chēng)Dynamic Network Representation Based on Latent Factorization of Tensors讀者反饋學(xué)科排名
作者: 怕失去錢(qián) 時(shí)間: 2025-3-22 00:10
L,strate that compared with several state-of-the-art models, the proposed ANLT model achieves significant gain in prediction accuracy and computational efficiency for predicting missing links of an HDI dynamic network.作者: Albumin 時(shí)間: 2025-3-22 02:02
Dynamic Network Representation Based on Latent Factorization of Tensors作者: 抱怨 時(shí)間: 2025-3-22 04:35
ADMM-Based Nonnegative Latent Factorization of Tensors,strate that compared with several state-of-the-art models, the proposed ANLT model achieves significant gain in prediction accuracy and computational efficiency for predicting missing links of an HDI dynamic network.作者: 夸張 時(shí)間: 2025-3-22 09:18 作者: Semblance 時(shí)間: 2025-3-22 15:20
Dynamic Network Representation Based on Latent Factorization of Tensors978-981-19-8934-6Series ISSN 2191-5768 Series E-ISSN 2191-5776 作者: Semblance 時(shí)間: 2025-3-22 19:46 作者: 數(shù)量 時(shí)間: 2025-3-22 21:17
I,ndled in a new low-dimensional space for further analysis [1–4]. This chapter provide an overview of dynamic network representation, including backgrounds, basic definitions, preliminaries, and organizations of this book.作者: 灰心喪氣 時(shí)間: 2025-3-23 04:15
J,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作者: DEFER 時(shí)間: 2025-3-23 06:45
K,Yet such an HDI tensor contains plenty of useful knowledge regarding various desired patterns like potential links in a dynamic network. An LFT model built by a Stochastic Gradient Descent (SGD) solver can acquire such knowledge from an HDI tensor. Nevertheless, an SGD-based LFT model suffers from s作者: 寄生蟲(chóng) 時(shí)間: 2025-3-23 10:38 作者: 殺人 時(shí)間: 2025-3-23 16:35 作者: 巨大沒(méi)有 時(shí)間: 2025-3-23 18:27
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作者: Allure 時(shí)間: 2025-3-24 01:39 作者: extrovert 時(shí)間: 2025-3-24 04:14
https://doi.org/10.1007/978-981-19-8934-6Dynamic network representation; Latent factorization of tensors; High-dimensional and incomplete tenso作者: ENACT 時(shí)間: 2025-3-24 08:03
978-981-19-8933-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: 軍械庫(kù) 時(shí)間: 2025-3-24 13:20
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作者: Hay-Fever 時(shí)間: 2025-3-24 15:26
SpringerBriefs in Computer Sciencehttp://image.papertrans.cn/e/image/283681.jpg作者: 圓柱 時(shí)間: 2025-3-24 20:54 作者: antedate 時(shí)間: 2025-3-25 02:36
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作者: Anecdote 時(shí)間: 2025-3-25 04:07
PID-Incorporated Latent Factorization of Tensors,Yet such an HDI tensor contains plenty of useful knowledge regarding various desired patterns like potential links in a dynamic network. An LFT model built by a Stochastic Gradient Descent (SGD) solver can acquire such knowledge from an HDI tensor. Nevertheless, an SGD-based LFT model suffers from s作者: 秘密會(huì)議 時(shí)間: 2025-3-25 07:49 作者: opalescence 時(shí)間: 2025-3-25 15:14
ADMM-Based Nonnegative Latent Factorization of Tensors,dynamic network is of the essence to effectively extract knowledge. Therefore, in order to accomplish precisely represent to an HDI dynamic network, this chapter present a novel .lternating direction method of multipliers (ADMM)-based Nonnegative Latent-factorization of Tensors (ANLT) model. It adop作者: angina-pectoris 時(shí)間: 2025-3-25 19:41
Perspectives and Conclusion,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作者: 主動(dòng) 時(shí)間: 2025-3-25 20:33 作者: optic-nerve 時(shí)間: 2025-3-26 01:52
J,odel. Empirical studies on two large-scale dynamic networks generated by industrial applications show that the proposed MBLFT model achieves higher prediction accuracy than state-of-the-art models in solving missing link prediction task.作者: 彎曲的人 時(shí)間: 2025-3-26 07:10 作者: BLUSH 時(shí)間: 2025-3-26 11:57 作者: 憂(yōu)傷 時(shí)間: 2025-3-26 15:13 作者: COMMA 時(shí)間: 2025-3-26 20:21 作者: Dysarthria 時(shí)間: 2025-3-26 21:50
K,ver to improve the convergence rate. Empirical studies on two large-scale dynamic networks generating from a real application show that the proposed PLFT model is superior to several state-of-the-art models in terms of convergence rate and computational efficiency when predicting missing directed and weighted links in a given dynamic network.作者: 吹牛者 時(shí)間: 2025-3-27 01:38
J,ain model and handle nonnegative constraints. The empirical studies on two dynamic network datasets show that the proposed DBNT model achieves higher prediction accuracy than state-of-the-art models when dealing with the missing link prediction task.作者: Mediocre 時(shí)間: 2025-3-27 08:45
PID-Incorporated Latent Factorization of Tensors,ver to improve the convergence rate. Empirical studies on two large-scale dynamic networks generating from a real application show that the proposed PLFT model is superior to several state-of-the-art models in terms of convergence rate and computational efficiency when predicting missing directed and weighted links in a given dynamic network.作者: 儀式 時(shí)間: 2025-3-27 10:13
Diverse Biases Nonnegative Latent Factorization of Tensors,ain model and handle nonnegative constraints. The empirical studies on two dynamic network datasets show that the proposed DBNT model achieves higher prediction accuracy than state-of-the-art models when dealing with the missing link prediction task.作者: Hemiplegia 時(shí)間: 2025-3-27 15:21
Richard H. Enns,George C. McGuire to compare my class to a neighbouring one, but when its teacher saw the success we were having she decided to introduce her own tricky word wall. It is exciting that my visible learning display encouraged these students to succeed, sparking their self-motivation and allowing them to take charge of their own learning.作者: 帶子 時(shí)間: 2025-3-27 19:47
Changing Conceptions of Multiculturalism,t, it no longer has any real meaning, other than at a very generalised level and in political terms. This chapter attempts to explain some of the various ways in which the term has been used and also suggests some new interpretations of ‘difference’ within modern multicultural democracies.作者: 等級(jí)的上升 時(shí)間: 2025-3-28 00:13 作者: 藕床生厭倦 時(shí)間: 2025-3-28 04:38
Ralf Dewenter,Jürgen R?schStellt sowohl die Theorie als auch die Anwendung dar.Hoher Praxisbezug durch übertragung auf reale Beispiele in der Medien?konomik.Fokus auf theoretischer Analyse sowie wettbewerbspolitischen/-rechtli
