Self-supervised learning with graph representations is a method where computer programs learn to recognize relationships and structures in connected data without needing many labeled examples.
A graph consists of nodes that represent things and edges that show connections between these things.
The program learns to understand such structures and discover important patterns by looking at the connections and neighborhoods of the nodes.
This way, the program can later process information better and solve tasks that depend on complex linkages, for example in social networks or molecular structures.