  Convolutional layers in graph neural networks are a fundamental type of layer
which output a representation or embedding of each graph vertex. The
representation typically encodes information about the vertex in question and
its neighbourhood. If one wishes to perform a graph centric task, such as graph
classification, this set of vertex representations must be integrated or pooled
to form a graph representation.
  In this article we propose a novel pooling method which maps a set of vertex
representations to a function space representation. This method is distinct
from existing pooling methods which perform a mapping to either a vector or
sequence space. Experimental graph classification results demonstrate that the
proposed method generally outperforms most baseline pooling methods and in some
cases achieves best performance.
