We present graph partition neural networks (GPNN), an extension of graph
neural 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...
partition graphs, we experiment with several partitioning algorithms and also
propose a novel variant for fast processing of large scale graphs. We
extensively test our model on a variety of semi-supervised node classification
tasks. Experimental results indicate that GPNNs are either superior or
comparable to state-of-the-art methods on a wide variety of datasets for
graph-based semi-supervised classification. We also show that GPNNs can achieve
similar performance as standard GNNs with fewer propagation steps.