Papers › PiNet: A Permutation Invariant Graph Neural Network for Graph Classification

PiNet: A Permutation Invariant Graph Neural Network for Graph Classification

8 May 2019arXiv:1905.03046archive 2025-07-28

Peter Meltzer, Marcelo Daniel Gutierrez Mallea, Peter J. Bentley

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.

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