  This work generalizes graph neural networks (GNNs) beyond those based on the
Weisfeiler-Lehman (WL) algorithm, graph Laplacians, and diffusions. Our
approach, denoted Relational Pooling (RP), draws from the theory of finite
partial exchangeability to provide a framework with maximal representation
power for graphs. RP can work with existing graph representation models and,
somewhat counterintuitively, can make them even more powerful than the original
WL isomorphism test. Additionally, RP allows architectures like Recurrent
Neural Networks and Convolutional Neural Networks to be used in a theoretically
sound approach for graph classification. We demonstrate improved performance of
RP-based graph representations over state-of-the-art methods on a number of
tasks.
