  Prior to the introduction of Graph Neural Networks (GNNs), modeling and
analyzing irregular data, particularly graphs, was thought to be the Achilles'
heel of deep learning. The core concept of GNNs is to find a representation by
recursively aggregating the representations of a central node and those of its
neighbors. The core concept of GNNs is to find a representation by recursively
aggregating the representations of a central node and those of its neighbor,
and its success has been demonstrated by many GNNs' designs. However, most of
them only focus on using the first-order information between a node and its
neighbors. In this paper, we introduce a central node permutation variant
function through a frustratingly simple and innocent-looking modification to
the core operation of a GNN, namely the Feature cOrrelation aGgregation (FOG)
module which learns the second-order information from feature correlation
between a node and its neighbors in the pipeline. By adding FOG into existing
variants of GNNs, we empirically verify this second-order information
complements the features generated by original GNNs across a broad set of
benchmarks. A tangible boost in performance of the model is observed where the
model surpasses previous state-of-the-art results by a significant margin while
employing fewer parameters. (e.g., 33.116% improvement on a real-world
molecular dataset using graph convolutional networks).

Machine Learning;Computer Vision and Pattern Recognition