The fully-supervised node classification loss is typically the negative log-likelihood of the true label under a softmax classifier applied to each node's embedding: $\mathcal{L} = -\sum_{v_i \in \mathcal{V}_{train}} \log\big(\text{softmax}(\text{MLP}(z_i))_{y_i}\big)$ where $z_i$ is the GNN-produced embedding for node $v_i$, $\mathcal{V}_{train}$ is the set of labeled training nodes, and $y_i$ is the true label of node $v_i$.