Applications of Graph Neural Networks – Towards Data Science

Graphs and their study have received a lot of attention since ages due to their ability of representing the real world in a fashion that can be analysed objectively. Indeed, graphs can be used to represent a lot of useful, real world datasets such as social networks, web link data, molecular structures, geographical maps, etc. Apart from these cases which have a natural structure to them, non-structured data such as images and text can also be modelled in the form of graphs in order to perform graph analysis on them. Due to the expressiveness of graphs and a tremendous increase in the available computational power in recent times, a good amount of attention has been directed towards the machine learning way of analysing graphs, i.e. According to this paper, Graph neural networks (GNNs) are connectionist models that capture the dependence of graphs via message passing between the nodes of graphs. They are extensions of the neural network model to capture the information represented as graphs.