  An effective aggregation of node features into a graph-level representation
via readout functions is an essential step in numerous learning tasks involving
graph neural networks. Typically, readouts are simple and non-adaptive
functions designed such that the resulting hypothesis space is permutation
invariant. Prior work on deep sets indicates that such readouts might require
complex node embeddings that can be difficult to learn via standard
neighborhood aggregation schemes. Motivated by this, we investigate the
potential of adaptive readouts given by neural networks that do not necessarily
give rise to permutation invariant hypothesis spaces. We argue that in some
problems such as binding affinity prediction where molecules are typically
presented in a canonical form it might be possible to relax the constraints on
permutation invariance of the hypothesis space and learn a more effective model
of the affinity by employing an adaptive readout function. Our empirical
results demonstrate the effectiveness of neural readouts on more than 40
datasets spanning different domains and graph characteristics. Moreover, we
observe a consistent improvement over standard readouts (i.e., sum, max, and
mean) relative to the number of neighborhood aggregation iterations and
different convolutional operators.

Machine Learning;Artificial Intelligence