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Dynamic Network Representation Based on Latent Factorization of Tensors

Part of the SpringerBriefs in Computer Science Book Series

DOI: 10.1007/978-981-19-8934-6

ISBN: 978-981-19-8933-9

Publisher: Springer Nature Singapore - Springer Journals

Mar 8, 2023

Available

Introduction

Wu, Hao; Wu, Xuke; Luo, Xin

Pages: 1 –10

[Dynamic network representation is to embed network vertices into low-dimensional vector space, thereby making an original dynamic network be easily handled in a new low-dimensional space for further analysis [1–4]. This chapter provide an overview of dynamic network representation, including...
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Multiple Biases-Incorporated Latent Factorization of Tensors

Wu, Hao; Wu, Xuke; Luo, Xin

Pages: 11 –26

[An HDI tensor modeling a dynamic network contains rich knowledge regarding dynamic network evolution. An LFT-based model has attracted plenty of attention on extracting useful knowledge form an HDI tensor. However, existing LFT-based models lack solid consideration for the volatility of dynamic...
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PID-Incorporated Latent Factorization of Tensors

Wu, Hao; Wu, Xuke; Luo, Xin

Pages: 27 –42

[A large-scale dynamic network is a fundamental data source in many big-data-related applications, which can be seamlessly described by an HDI tensor. Yet such an HDI tensor contains plenty of useful knowledge regarding various desired patterns like potential links in a dynamic network. An LFT...
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Diverse Biases Nonnegative Latent Factorization of Tensors

Wu, Hao; Wu, Xuke; Luo, Xin

Pages: 43 –56

[How to precisely capture temporal pattern hidden in a dynamic network is crucial to acquire rich knowledge from an HDI dynamic network with directed and weighted links. An HDI tensor can seamlessly describe a HDI dynamic network. Thus, building a HDI tensor learning model that can capture...
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ADMM-Based Nonnegative Latent Factorization of Tensors

Wu, Hao; Wu, Xuke; Luo, Xin

Pages: 57 –76

[An HDI dynamic network contains a multitude of knowledge regarding involved nodes’ various behavior patterns. How to accurately represent such an HDI dynamic network is of the essence to effectively extract knowledge. Therefore, in order to accomplish precisely represent to an HDI dynamic...
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Perspectives and Conclusion

Wu, Hao; Wu, Xuke; Luo, Xin

Pages: 77 –80

[Tensor and tensor decomposition are very versatile and powerful tools, which has a wide range of applications in data mining, signal processing, computer vision and other fields [1–5]. For a third-order HDI tensor modeling a dynamic network, this book carry out some preliminary research on...

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