We present graph partition neural networks (GPNN), an extension of graph neu- ral networks (GNNs) able to handle extremely large graphs. GPNNs alternate between locally propagating information between nodes in small subgraphs and globally propagating information between the subgraphs. To efficiently partition graphs, we experiment with spectral partition and also propose a modified multi- seed flood fill for fast processing large scale graphs. We extensively test our model on a variety of semi-supervised node classification tasks. First, we show that GPNNs can achieve similar performance as standard GNNs with fewer propaga- tion steps. Secondly, experimental results indicate that GPNNs results are compa- rable to a wide selection of baselines on medium-sized datasets and superior on a very large dataset.