Learning from small number of examples

[sujeongkim]

I've tried training the network on a single graph. (Training on a single graph and testing on the same graph)
I expected that the network would almost memorize the graph so the loss would be very close to zero. Surprisingly, the loss was about 0.06-0.07, which is more than 20-30 times higher than when I train on 1000 samples). Test loss sometimes is much larger.
I repeated this experiment varying the number of nodes in the graph from 4 to 200, and they all look similar.
I don't have much experience in GNNs and machine learning, but is this normal?