Self-supervised learning with graphs is a method where computers learn to recognize relationships in complex networks without being told all the answers beforehand.
Imagine you have a large network of friends who know each other and want to find out who belongs to which group or who has similar interests – all without anyone telling you what the groups are.
The computer uses the structure of the network, that is, how the nodes (for example, people) are connected, to independently discover patterns and similarities.
This way, it can predict new connections, identify important nodes, or understand complex structures – all without many labeled examples.