Graph Neural Networks (GNNs) fall into the categories of Artificial Intelligence, Big Data and Smart Data, and Industry and Factory 4.0. They are a special type of artificial neural network that can particularly well handle information organised in the form of networks or relationships \u2013 so-called \u201egraphs\u201c. Examples of these include social networks, supply chains, or communication structures within companies.<\/p>\n
Imagine you want to predict how information spreads in a large company. GNNs don't just analyse individual employees, but also how they are connected and how they communicate with each other. This allows them to discover patterns in how knowledge is shared or how quickly news spreads.<\/p>\n
A practical example from industry: a machine manufacturer wants to identify which spare parts in a factory network are most likely to fail first. Using Graph Neural Networks, not only individual machines are considered, but also their connections to each other, enabling precise predictions and optimisations for maintenance and production. <\/p>\n
Graph Neural Networks are therefore particularly valuable when relationships between individual elements are to be analysed \u2013 a key technology for modern data analyses and networked systems.<\/p>","protected":false},"excerpt":{"rendered":"