Graph neural networks (GNNs) are being used to process data represented as graphs.A new perspective on the representational capability of GNNs is explored.A soft-injective function and a soft-isomorphic relational graph convolution network (SIR-GCN) are proposed.Experiments show that SIR-GCN outperforms comparable models in prediction tasks.