An introduction to Graph Neural Networks
Neural Networks aimed at effectively handling graph data.Photo by Alina Grubnyak on UnsplashGraph structured data is common across various domains, examples such as molecules, { social, citation, road } networks, are just a few of the vast array of data which can be represented with a graphs. With the advancements of machine learning we witness the potential for applying intelligent algorithms on the data which is available. Graph Neural Network is the branch of Machine Learning which concerns on building neural networks for graph data in the most effective manner.Notwithstanding the progress made with ML in the computer vision domain with convolutional networks, Graph Neural Networks (GNNs) face a more challenging problem, they deal with the awkward nature of graphs. Differently from images and text, graphs do not have a well defined structure. A graph’s node might have no connections or many, of which could be directed or undirected. Graphs in a dataset may have a variable