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008			230307s2023 si | fo |||| 0|eng d
020			_a9789811989346
024	7		_a10.1007/978-981-19-8934-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK5105.5. _b2023 EB
100	1		_aWu, Hao _eautor _4http://id.loc.gov/vocabulary/relators/aut _984950
245	1	0	_aDynamic Network Representation Based on Latent Factorization of Tensors _cby Hao Wu, Xuke Wu, Xin Luo
250			_a1st ed 2023
264		1	_aSingapore _bSpringer Nature _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aSpringerBriefs in Computer Science _x2191-5776
505	0		_aChapter 1 IntroductionChapter -- 2 Multiple Biases-Incorporated Latent Factorization of tensors -- Chapter 3 PID-Incorporated Latent Factorization of Tensors -- Chapter 4 Diverse Biases Nonnegative Latent Factorization of Tensors -- Chapter 5 ADMM-Based Nonnegative Latent Factorization of Tensors -- Chapter 6 Perspectives and Conclusion. .
520			_aA dynamic network is frequently encountered in various real industrial applications, such as the Internet of Things. It is composed of numerous nodes and large-scale dynamic real-time interactions among them, where each node indicates a specified entity, each directed link indicates a real-time interaction, and the strength of an interaction can be quantified as the weight of a link. As the involved nodes increase drastically, it becomes impossible to observe their full interactions at each time slot, making a resultant dynamic network High Dimensional and Incomplete (HDI). An HDI dynamic network with directed and weighted links, despite its HDI nature, contains rich knowledge regarding involved nodes' various behavior patterns. Therefore, it is essential to study how to build efficient and effective representation learning models for acquiring useful knowledge. In this book, we first model a dynamic network into an HDI tensor and present the basic latent factorization of tensors (LFT) model. Then, we propose four representative LFT-based network representation methods. The first method integrates the short-time bias, long-time bias and preprocessing bias to precisely represent the volatility of network data. The second method utilizes a proportion-al-integral-derivative controller to construct an adjusted instance error to achieve a higher convergence rate. The third method considers the non-negativity of fluctuating network data by constraining latent features to be non-negative and incorporating the extended linear bias. The fourth method adopts an alternating direction method of multipliers framework to build a learning model for implementing representation to dynamic networks with high preciseness and efficiency.
988			_aSpringer_Computer_2023
650		7	_2embne _9139268 _aInformática
700	1		_9689946 _aWu, Xuke _eautor
700	1		_9689931 _aLuo, Xin _c(profesor) _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-8934-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2024 _dz _ean _zSI