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Graph property prediction is drawing increasing attention in the recent yearsdue to the fact that graphs are one of the most general data structures sincethey can contain an arbitrary number of nodes and connections between them, andit is the backbone for many different tasks like classification and regressionon such kind of data (networks, molecules, knowledge bases, ...). We introducea novel generalized global pooling layer to mitigate the information loss thattypically occurs at the Readout phase in Message-Passing Neural Networks. Thisnovel layer is parametrized by two values ($\beta$ and $p$) which canoptionally be learned, and the transformation it performs can revert to severalalready popular readout functions (mean, max and sum) under certain settings,which can be specified. To showcase the superior expressiveness and performanceof this novel technique, we test it in a popular graph property prediction taskby taking the current best-performing architecture and using our readout layeras a drop-in replacement and we report new state of the art results. The codeto reproduce the experiments can be accessed here:https://github.com/EricAlcaide/generalized-readout-phase

Improving Graph Property Prediction with Generalized Readout Functions