Intro
Preface
Contents
Chapter 1: Introduction
1.1 Overview
1.2 Formulating a Dynamic Network into an HDI Tensor
1.3 Latent Factorization of Tensor
1.4 Book Organization
References
Chapter 2: Multiple Biases-Incorporated Latent Factorization of Tensors
2.1 Overview
2.2 MBLFT Model
2.2.1 Short-Term Bias
2.2.2 Preprocessing Bias
2.2.3 Long-Term Bias
2.2.4 Parameter Learning Via SGD
2.3 Performance Analysis of MBLFT Model
2.3.1 MBLFT Algorithm Design
2.3.2 Effect of Short-Term Bias
2.3.3 Effect of Preprocessing Bias

2.3.4 Effect of Long-Term Bias
2.3.5 Comparison with State-of-the-Art Models
2.4 Summary
References
Chapter 3: PID-Incorporated Latent Factorization of Tensors
3.1 Overview
3.2 PLFT Model
3.2.1 A PID Controller
3.2.2 Objective Function
3.2.3 Parameter Learning Scheme
3.3 Performance Analysis of PLFT Model
3.3.1 PLFT Algorithm Design
3.3.2 Effects of Hyper-Parameters
3.3.3 Comparison with State-of-the-Art Models
3.4 Summary
References
Chapter 4: Diverse Biases Nonnegative Latent Factorization of Tensors
4.1 Overview
4.2 DBNT Model

4.2.1 Extended Linear Biases
4.2.2 Preprocessing Bias
4.2.3 Parameter Learning Via SLF-NMU
4.3 Performance Analysis of DBNT Model
4.3.1 DBNT Algorithm Design
4.3.2 Effects of Biases
4.3.3 Comparison with State-of-the-Art Models
4.4 Summary
References
Chapter 5: ADMM-Based Nonnegative Latent Factorization of Tensors
5.1 Overview
5.2 ANLT Model
5.2.1 Objective Function
5.2.2 Learning Scheme
5.2.3 ADMM-Based Learning Sequence
5.3 Performance Analysis of ANLT Model
5.3.1 ANLT Algorithm Design
5.3.2 Comparison with State-of-the-Art Models

5.4 Summary
References
Chapter 6: Perspectives and Conclusion
6.1 Perspectives
6.2 Conclusion
References