Graph Neural Networks were introduced back in 2005 (like all the other good ideas) but they started to gain popularity in the last 5 years. The GNNs are able to model the relationship between the nodes in a graph and produce a numeric representation of it. The importance of GNNs is quite significant because there are so many real-world data that can be represented as a graph. Social networks, chemical compounds, maps, transportation systems to name a few. So let’s find out the basic principles behind GNNs and why they work.
Source: Graph Neural Networks - An overview, an article by Sergios Karagiannakos.