  Graph neural networks (GNNs) are a class of neural networks that allow to
efficiently perform inference on data that is associated to a graph structure,
such as, e.g., citation networks or knowledge graphs. While several variants of
GNNs have been proposed, they only consider simple nonlinear activation
functions in their layers, such as rectifiers or squashing functions. In this
paper, we investigate the use of graph convolutional networks (GCNs) when
combined with more complex activation functions, able to adapt from the
training data. More specifically, we extend the recently proposed kernel
activation function, a non-parametric model which can be implemented easily,
can be regularized with standard $\ell_p$-norms techniques, and is smooth over
its entire domain. Our experimental evaluation shows that the proposed
architecture can significantly improve over its baseline, while similar
improvements cannot be obtained by simply increasing the depth or size of the
original GCN.
