Graph Partition Neural Networks for Semi-Supervised Classification
This addresses scalability issues in graph neural networks for semi-supervised classification tasks, offering an incremental improvement over existing methods.
The paper tackles the problem of handling extremely large graphs in semi-supervised classification by proposing Graph Partition Neural Networks (GPNNs), which alternate local and global propagation in partitioned subgraphs, achieving superior or comparable performance to state-of-the-art methods on various datasets and reducing propagation steps compared to standard GNNs.
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. To efficiently 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.