標(biāo)題: Titlebook: Machine Learning in Social Networks; Embedding Nodes, Edg Manasvi Aggarwal,M.N. Murty Book 2021 The Author(s), under exclusive license to S [打印本頁(yè)] 作者: 使無(wú)罪 時(shí)間: 2025-3-21 19:22
書(shū)目名稱Machine Learning in Social Networks影響因子(影響力)
書(shū)目名稱Machine Learning in Social Networks影響因子(影響力)學(xué)科排名
書(shū)目名稱Machine Learning in Social Networks網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Machine Learning in Social Networks網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Machine Learning in Social Networks被引頻次
書(shū)目名稱Machine Learning in Social Networks被引頻次學(xué)科排名
書(shū)目名稱Machine Learning in Social Networks年度引用
書(shū)目名稱Machine Learning in Social Networks年度引用學(xué)科排名
書(shū)目名稱Machine Learning in Social Networks讀者反饋
書(shū)目名稱Machine Learning in Social Networks讀者反饋學(xué)科排名
作者: laparoscopy 時(shí)間: 2025-3-21 20:18 作者: 平 時(shí)間: 2025-3-22 00:42
https://doi.org/10.1007/978-981-33-4022-0Network embedding; Deep Learning (DL); Neural Networks; Network representation learning; Embedded graphs作者: placebo 時(shí)間: 2025-3-22 05:02
Embedding Graphs,There are several applications where an embedding or a low-dimensional representation of the entire graph is required. This chapter deals with such representations which are called .. We consider various state-of-the-art graph pooling techniques that are important in this context. We also consider . tasks including ., and 作者: Glossy 時(shí)間: 2025-3-22 09:45
Conclusions,this book we have examined .,?and their analysis. Specifically, we considered the following aspects.作者: 合同 時(shí)間: 2025-3-22 13:44
978-981-33-4021-3The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021作者: Infraction 時(shí)間: 2025-3-22 17:04
Machine Learning in Social Networks978-981-33-4022-0Series ISSN 2191-530X Series E-ISSN 2191-5318 作者: 山間窄路 時(shí)間: 2025-3-23 01:11 作者: 昏睡中 時(shí)間: 2025-3-23 03:09 作者: 比喻好 時(shí)間: 2025-3-23 09:33 作者: 狗舍 時(shí)間: 2025-3-23 11:34
Manasvi Aggarwal,M. N. Murtyow-grade bauxite ore which is commonly used for the alumina-based abrasives and refractories productions. The alumina-silica and alumina-ferrite complexes are the foremost impurities present in the low-grade bauxite. They affect its commercial utilities due to development of poor binding property in作者: Watemelon 時(shí)間: 2025-3-23 14:09 作者: EVICT 時(shí)間: 2025-3-23 22:07
Manasvi Aggarwal,M. N. Murtyrbonization process of calcified slag in the process was mainly studied in this paper, that is, the hydrogarnet in the calcified slag was decomposed by CO. at high temperature and high pressure. Firstly, the water model experiments were conducted under different ventilation modes to investigate the 作者: 思想上升 時(shí)間: 2025-3-24 01:27 作者: BILE 時(shí)間: 2025-3-24 03:54
Manasvi Aggarwal,M. N. Murtyscarce, mining areas with poor-settling high phosphorus bauxites are being explored. In preparation for having to process these bauxites, this study sought to identify the main minerals that influence soluble phosphorus concentrations. Four correlations for predicting soluble phosphorus during low t作者: 伙伴 時(shí)間: 2025-3-24 09:27 作者: modifier 時(shí)間: 2025-3-24 10:50 作者: 東西 時(shí)間: 2025-3-24 14:56 作者: Genistein 時(shí)間: 2025-3-24 19:40 作者: OUTRE 時(shí)間: 2025-3-25 02:50 作者: consolidate 時(shí)間: 2025-3-25 05:59 作者: Minikin 時(shí)間: 2025-3-25 07:32
2191-530X oth conventional machine learning (ML) and deep learning (DL.This book deals with?network?representation learning. It deals with embedding nodes, edges, subgraphs and graphs. There is a growing interest in understanding complex systems in different domains including health, education, agriculture an作者: Obedient 時(shí)間: 2025-3-25 14:10
Book 2021rstanding complex systems in different domains including health, education, agriculture and transportation. Such complex systems are analyzed by?modeling, using networks that are aptly called complex networks. Networks are becoming ubiquitous as they can represent many real-world relational data, fo作者: nonplus 時(shí)間: 2025-3-25 16:31
Introduction,is prompted recent growth in network embedding tools and techniques to present the underlying data in a simpler form for analysis. In this chapter the notion of embedding?is introduced. Also how an embedding helps in overcoming some of the problems associated with the analysis of large high-dimensional networks.作者: Jubilation 時(shí)間: 2025-3-25 22:46
Node Representations, embedding techniques. These techniques are based on one of random walk, matrix factorization, or deep learning. Further, some algorithms learn representations in an unsupervised setting while others learn in a supervised setting. We finally present comparison of these algorithms according to their performance on downstream tasks.作者: Cardiac 時(shí)間: 2025-3-26 00:54 作者: 妨礙議事 時(shí)間: 2025-3-26 07:30
on rate between gas and liquid. Secondly, the influence of these factors on the carbonization process of calcified slag was verified in the 2?L high temperature and pressure reactor. The optimum experimental conditions were obtained by measuring the carbon content of the slag in the reaction process作者: PLIC 時(shí)間: 2025-3-26 12:32 作者: HEAVY 時(shí)間: 2025-3-26 13:39 作者: Exposure 時(shí)間: 2025-3-26 17:57 作者: Bother 時(shí)間: 2025-3-26 23:39
Manasvi Aggarwal,M. N. Murtynot explored for hydrometallurgical reactions in the literature. The Brazilian sample was leached by sulfuric acid 20%. The solid/liquid ratio was 1/10 and samples were analyzed in different time reactions at 25?°C. Results showed that the mineral phases obtained were in agreement with the thermodyn作者: Complement 時(shí)間: 2025-3-27 05:05
Manasvi Aggarwal,M. N. Murtypositions. If the phosphorus impurity in the future bauxite is taken as crandallite, a correlation that over-predicts the measured soluble phosphorus by about 20–30% can be used to assess lime requirements for phosphorus control. Crandallite, calcite and silica were the main minerals that influence 作者: Heresy 時(shí)間: 2025-3-27 09:10 作者: PLIC 時(shí)間: 2025-3-27 11:00 作者: 萬(wàn)神殿 時(shí)間: 2025-3-27 15:28
Book 2021t information of networks. An active and important area ofcurrent interest is to come out with algorithms that learn features by embedding nodes or (sub)graphs into a vector space. These tasks come under the broad umbrella of representation learning. A representation learning model learns a mapping 作者: Thyroid-Gland 時(shí)間: 2025-3-27 18:39
10樓作者: apropos 時(shí)間: 2025-3-27 22:21
10樓
