Many relationships among data in several areas (such as computer vision,
molecular chemistry and pattern recognition) can be represented by
graphs. In the machine learning setting, it is an important learning
task to classify graph-structural data correctly. Typically, the
established techniques for this setting proceed via graph kernels and
neural-network classification. In this work, we explore end-to-end
learning for graphs: the objective is to operate on the graph
representations directly. The key idea of our approach is to use
standard tools for graph canonization. We test the performance of this
approach on several datasets arising from bioinformatics. In general, we
find that the graph canonization, as such, does not improve the accuracy
of the classification. A possible reason for this behavior is that the
neural network ends up overfitting to the given adjacency matrix
representation.