Graph embeddings are a term from the fields of Artificial Intelligence, Big Data and Smart Data, and Digital Transformation. They describe a method of representing large amounts of complex data from so-called networks or „graphs“ – systems of different points (e.g., people, devices, or websites) that are interconnected – in such a way that computers can process and analyse them more easily.
For example, imagine a social network like LinkedIn: each user is a dot, and the connections to other users are the lines between them. Graph embeddings translate this network into series of numbers that are understandable to computers. This allows artificial intelligence to quickly recognise similarities, suggest new connections, or even uncover suspicious patterns.
A practical example: a company wants to find out which employees could work particularly well together. With Graph Embeddings, the software can analyse relationship data and make concrete suggestions about who could form a good team – even in very large organisations.
Graph embeddings therefore help to obtain easily understandable answers from complex relational data for various applications in the digital world.
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