Graph Convolutional Networks (GCN) Explained At High Level

In this article, we will understand why graphical data are essential and how they can be processed with graph neural networks, and we will see how they are used in drug repositioning. The unique capability of graphs enables capturing the structural relations among data and thus allows for harvesting more insights than analyzing data in isolation. Graphs are among the most versatile data structures. They naturally appear in numerous application domains, ranging from social analysis, bioinformatics to computer vision. Euclidean data is modeled as being plotted in n-dimensional linear space.