  Modern data sources are typically of large scale and multi-modal natures, and
acquired on irregular domains, which poses serious challenges to traditional
deep learning models. These issues are partially mitigated by either extending
existing deep learning algorithms to irregular domains through graphs, or by
employing tensor methods to alleviate the computational bottlenecks imposed by
the Curse of Dimensionality. To simultaneously resolve both these issues, we
introduce a novel Multi-Graph Tensor Network (MGTN) framework, which leverages
on the desirable properties of graphs, tensors and neural networks in a
physically meaningful and compact manner. This equips MGTNs with the ability to
exploit local information in irregular data sources at a drastically reduced
parameter complexity, and over a range of learning paradigms such as
regression, classification and reinforcement learning. The benefits of the MGTN
framework, especially its ability to avoid overfitting through the inherent
low-rank regularization properties of tensor networks, are demonstrated through
its superior performance against competing models in the individual tensor,
graph, and neural network domains.
