Bi, F., He, T., Ong, Y.-S., Luo, X. (2025). Discovering Spatiotemporal–Individual Coupled Features From Nonstandard Tensors—A Novel Dynamic Graph Mixer Approach. IEEE Transactions on Neural Networks and Learning Systems, 36(11), 19834–19848. https://doi.org/10.1109/tnnls.2025.3592692
Abstract:
In this article, we present the dynamic graph mixer (DGM), a novel model for learning spatiotemporal-individual coupled features from high-dimensional and incomplete (HDI) tensors, which frequently represent dynamic interactions among real-world data samples. In contrast to existing methods, the proposed DGM possesses the following three advantages when learning representations from HDI tensors. First, it performs light graph message passing based on the conjoint attentions learned by jointly modeling latent features and implicit structures to extract the high-order connectivity. Second, a multilayer nonlinear tensor neural network (TNN) is adopted to learn the intricate attribute features of node–node–time from different views. Third, it follows the Tucker decomposition paradigm in a data density-oriented modeling mechanism to integrate node representations, preserving the overall multidimensional interaction patterns. In addition, we provide theoretical evidence that the key components in DGM can significantly improve expressiveness. Extensive experiments conducted on eight testing datasets of HDI tensors demonstrate that DGM outperforms state-of-the-art methods in both learning accuracy and efficiency.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the NRF - AI-based urban cooling technology development
Grant Reference no. : AISG3-TC-2024-014-SGKR