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