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Dynamic network representation based on latent factorization of tensors

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Dynamic network representation based on latent factorization of tensorsby Hao Wu, Xuke Wu, Xin Luo.
作者:	Wu, Hao.
其他作者:	Wu, Xuke.
出版者:	Singapore :Springer Nature Singapore :2023.
面頁冊數:	viii, 80 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Computer networksMathematical models.
電子資源:	https://doi.org/10.1007/978-981-19-8934-6
ISBN:	9789811989346$q(electronic bk.)

Dynamic network representation based on latent factorization of tensors
Wu, Hao.

Dynamic network representation based on latent factorization of tensors[electronic resource] /by Hao Wu, Xuke Wu, Xin Luo. - Singapore :Springer Nature Singapore :2023. - viii, 80 p. :ill. (chiefly col.), digital ;24 cm. - SpringerBriefs in computer science,2191-5776. - SpringerBriefs in computer science..

Chapter 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.

A 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.

ISBN: 9789811989346$q(electronic bk.)

Standard No.: 10.1007/978-981-19-8934-6doiSubjects--Topical Terms:

224514
Computer networks
--Mathematical models.

LC Class. No.: TK5105.5

Dewey Class. No.: 004.6

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505 0 $a Chapter 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 $a A 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.
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