What this represents
This is not meant to be a full GNN implementation. It shows the core intuition: a node starts with information, then its local neighborhood receives progressively weaker traces of it.
In a real graph neural network, each node repeatedly aggregates information from its neighbors and updates its representation. More hops means a wider receptive field, but also more smoothing.
The follow-up GNN toy makes that update step explicit with editable node features and one to three message-passing layers.