  The irregular and multi-modal nature of numerous modern data sources poses
serious challenges for traditional deep learning algorithms. To this end,
recent efforts have generalized existing algorithms to irregular domains
through graphs, with the aim to gain additional insights from data through the
underlying graph topology. At the same time, tensor-based methods have
demonstrated promising results in bypassing the bottlenecks imposed by the
Curse of Dimensionality. In this paper, we introduce a novel Multi-Graph Tensor
Network (MGTN) framework, which exploits both the ability of graphs to handle
irregular data sources and the compression properties of tensor networks in a
deep learning setting. The potential of the proposed framework is demonstrated
through an MGTN based deep Q agent for Foreign Exchange (FOREX) algorithmic
trading. By virtue of the MGTN, a FOREX currency graph is leveraged to impose
an economically meaningful structure on this demanding task, resulting in a
highly superior performance against three competing models and at a drastically
lower complexity.
