Graph Neural Networks (GNNs) have been successfully used in many problems involving graph-structured data, achieving state-of-the-art performance. GNNs typically employ a message-passing scheme, which every node aggregates information from its neighbors using permutation-invariant aggregation function. Standard well-examined choices such as the mean or sum functions limited capabilities, they a...