Link Prediction for Dynamic Weighted Graph via Adaptive Nonnegative Tensor CP Decomposition

A dynamic weighted graph is frequently encountered in real-world industrial applications like Internet of Things, which can be modeled into a Third-order Incomplete (ToI) tensor. Correspondingly, each element of the ToI tensor represents an observed link of dynamic weighted graph. A nonnegative tensor CP decomposition (NTC)-based link prediction model has proven to be efficient in predicting the missing links of a dynamic weighted graph. However, the learning objective of existing NTC model is usually built via a standard Euclidean distance, which restricts model prediction ability due to its low generalization. To address this issue, this paper presents an Adaptive Nonnegative Tensor CP decomposition (ANTC) model with two ideas include: a) adopting the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\beta$</tex> -divergence to build the learning objective for improving the generalization of the model; and b) implementing hyper-parameters self-adaptation via utilizing a differential evolutionary algorithm. Empirical studies on four dynamic weighted graphs generated by a real application illustrate that the proposed ANTC model achieves higher prediction accuracy and computational efficiency than state-of-the-art predictors in predicting the missing links.