Titlebook: Dynamic Network Representation Based on Latent Factorization of Tensors; Hao Wu,Xuke Wu,Xin Luo Book 2023 The Editor(s) (if applicable) an

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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

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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

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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

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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.

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