Classifying Me Softly: A Novel Graph Neural Network Based on Features Soft-Alignment

Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning. In this paper we propose a new graph neural network architecture based on the soft-alignment of the graph node features against sets of learned points. In each layer of the network the input node features are transformed by computing their similarity with respect to a set of learned features. The similarity information is then propagated to other nodes in the network, effectively creating a message passing-like mechanism where each node of the graph individually learns what is the optimal message to pass to its neighbours. We perform an ablation study to evaluate the performance of the network under different choices of its hyper-parameters. Finally, we test our model on standard graph-classification benchmarks and we find that it outperforms widely used alternative approaches, including both graph kernels and graph neural networks.