  We propose PiNet, a generalised differentiable attention-based pooling
mechanism for utilising graph convolution operations for graph level
classification. We demonstrate high sample efficiency and superior performance
over other graph neural networks in distinguishing isomorphic graph classes, as
well as competitive results with state of the art methods on standard
chemo-informatics datasets.

Machine Learning;Computer Vision and Pattern Recognition;Machine Learning