  We propose an end-to-end deep learning learning model for graph
classification and representation learning that is invariant to permutation of
the nodes of the input graphs. We address the challenge of learning a fixed
size graph representation for graphs of varying dimensions through a
differentiable node attention pooling mechanism. In addition to a theoretical
proof of its invariance to permutation, we provide empirical evidence
demonstrating the statistically significant gain in accuracy when faced with an
isomorphic graph classification task given only a small number of training
examples. We analyse the effect of four different matrices to facilitate the
local message passing mechanism by which graph convolutions are performed vs. a
matrix parametrised by a learned parameter pair able to transition smoothly
between the former. Finally, we show that our model achieves competitive
classification performance with existing techniques on a set of molecule
datasets.
