The current availability of powerful computers and huge data sets is creating new opportunities in computational mathematics to bring together concepts and tools from tensor algebra, graph theory, machine learning and signal processing. In discrete mathematics, a tensor is merely a collection of points (nodes in a graph) which are arranged as a multiway arrray. The power of such tensors lies in the fact that they can represent entities as diverse as the users of social networks or financial market data, and that these can be transformed into low-dimensional signals which can be analyzed using data analytics tools. In this talk, we aim to provide a comprehensive introduction to advanced data analytics on graphs using tensor. We will then establish a relationship between tensors and graphs, in order to move beyond the standard regular sampling in time and space. This facilitates modelling in many important areas, including communication networks, computer science, linguistics, social sciences, biology, physics, chemistry, transport, town planning, financial systems, personal health and many others. The tensor and graph topologies will be revisited from a modern data analytics point of view, and we will then proceed to establish a taxonomy of graph tensor networks. With this as a basis, we show such a framework allows for even the most challenging machine learning tasks, such as clustering, being performed in an intuitive and physically meaningful way. Unique aspects of the multi-graph tensor networks (MGTN) framework will be outlined, such as their benefits for processing data acquired on irregular domains, their ability to finely-tune statistical learning procedures through local information processing, the concepts of random signals on graphs and tensors, learning of graph topology from data observed on graphs, and confluence with deep neural networksnd Big Data. Extensive examples are included to render the concepts more concrete and to facilitate a greater understanding of the underlying principles.