This work generalizes graph neural networks (GNNs) beyond those based on the Weisfeiler-Lehman (WL) algorithm, graph Laplacians, and graph diffusion kernels. Our approach, denoted relational pooling (RP), draws from the theory of finite partial exchangeability to provide a framework wi