It's a generalization of a fully connected layer, where you give the network an adjacency matrix for data routing between nodes. This allows for designing sparse data flows and learning relations between pairs of input elements.

Graph NN layer: f(X, A) = g(AXW), where g is an activation

Fully connected layer: f(X) = g(XW), like GNN where the adj. matrix A=I

To put it in context a few years ago GNNs became a hot field. They are very similar in a way to transformers because both do pairwise interactions between elements, the difference being that GNNs use explicit and transformers implicit graphs.