  Graph classification is a significant problem in many scientific domains. It
addresses tasks such as the classification of proteins and chemical compounds
into categories according to their functions, or chemical and structural
properties. In a supervised setting, this problem can be framed as learning the
structure, features and relationships between features within a set of labelled
graphs and being able to correctly predict the labels or categories of unseen
graphs.
  A significant difficulty in this task arises when attempting to apply
established classification algorithms due to the requirement for fixed size
matrix or tensor representations of the graphs which may vary greatly in their
numbers of nodes and edges. Building on prior work combining explicit tensor
representations with a standard image-based classifier, we propose a model to
perform graph classification by extracting fixed size tensorial information
from each graph in a given set, and using a Capsule Network to perform
classification.
  The graphs we consider here are undirected and with categorical features on
the nodes. Using standard benchmarking chemical and protein datasets, we
demonstrate that our graph Capsule Network classification model using an
explicit tensorial representation of the graphs is competitive with current
state of the art graph kernels and graph neural network models despite only
limited hyper-parameter searching.
