A Novel Tensor Causal Convolution Network Model for Highly-Accurate Representation to Spatio-Temporal Data

Spatio-temporal data like a Dynamically Weighted Directed Network (DWDN) are ubiquitous in bigdata applications like an intelligent recommender system. They commonly illustrate the complex yet dynamic interactions between tremendous nodes, as well as contain rich knowledge regarding the involved nodes’ behavioral patterns with time dynamics. On the other hand, since the nodes constantly increase as the time accumulates, a DWDN becomes High-Dimensional and Incomplete (HDI) due to the limited interactions, widely-spread time slots and huge node count. Moreover, the inner temporal-spatio patterns exhibit strong nonlinearity, making it very difficult to grasp them from HDI data. To address this critical issue, this paper proposes a novel Tensor Causal Convolution Network (TCCN) model with three-fold ideas: a) innovatively building a feedforward tensor neural network with the incorporation of the Latent Factorization of Tensors (LFT) principle to model complex nonlinear interactions in an HDI DWDN efficiently; b) establishing a Tensor Causal Convolution (TCC) structure, which is able to accurately fuse the time-varying information from node interactions with high scalability; and c) developing a neighborhood regularization scheme to boost the local structural representation, thus capturing spatial dependencies among tremendous nodes. Extensively experimental results on ten real DWDNs from real bigdata applications evidently demonstrate that our proposed TCCN model outperforms several state-of-the-art models in both representation learning accuracy and convergence ability. It provides a highly-efficient representation learning approach for diverse bigdata applications.