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

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.