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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L,strate that compared with several state-of-the-art models, the proposed ANLT model achieves significant gain in prediction accuracy and computational efficiency for predicting missing links of an HDI dynamic network.

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Dynamic Network Representation Based on Latent Factorization of Tensors

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ADMM-Based Nonnegative Latent Factorization of Tensors,strate that compared with several state-of-the-art models, the proposed ANLT model achieves significant gain in prediction accuracy and computational efficiency for predicting missing links of an HDI dynamic network.

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Dynamic Network Representation Based on Latent Factorization of Tensors978-981-19-8934-6Series ISSN 2191-5768 Series E-ISSN 2191-5776

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I,ndled in a new low-dimensional space for further analysis [1–4]. This chapter provide an overview of dynamic network representation, including backgrounds, basic definitions, preliminaries, and organizations of this book.

灰心丧气

J,tion on extracting useful knowledge form an HDI tensor. However, existing LFT-based models lack solid consideration for the volatility of dynamic network data, thereby leading to the descent of model representation learning ability. To tackle this problem, this chapter proposes a multiple biases-inc

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K,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