# A PID-Controlled Tensor Wheel Decomposition Model for Dynamic Link Prediction

arXiv: https://arxiv.org/abs/2505.14211 · HTML: https://mlsanity.com/paper/2505.14211/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 107 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 3 of 3)

- [Machine learning with tree tensor networks, CP rank constraints, and tensor dropout](https://mlsanity.com/paper/2305.19440/) (2023)
- [QoS-Driven Resource Optimization for Intelligent Fog Radio Access Network: A Dynamic Power Allocation Perspective](https://mlsanity.com/paper/2103.02134/) (2021)
- [Bayesian Robust Tensor Ring Model for Incomplete Multiway Data](https://mlsanity.com/paper/2202.13321/) (2022)

## Abstract (arXiv)

Link prediction in dynamic networks remains a fundamental challenge in network science, requiring the inference of potential interactions and their evolving strengths through spatiotemporal pattern analysis. Traditional static network methods have inherent limitations in capturing temporal dependencies and weight dynamics, while tensor-based methods offer a promising paradigm by encoding dynamic networks into high-order tensors to explicitly model multidimensional interactions across nodes and time. Among them, tensor wheel decomposition (TWD) stands out for its innovative topological structure, which decomposes high-order tensors into cyclic factors and core tensors to maintain structural integrity. To improve the prediction accuracy, this study introduces a PID-controlled tensor wheel decomposition (PTWD) model, which mainly adopts the following two ideas: 1) exploiting the representation power of TWD to capture the latent features of dynamic network topology and weight evolution, and 2) integrating the proportional-integral-derivative (PID) control principle into the optimization process to obtain a stable model parameter learning scheme. The performance on four real datasets verifies that the proposed PTWD model has more accurate link prediction capabilities compared to other models.