Before revision:  In an effort to improve generalization of GNNs we suggest the Low-Rank Global Attention (LRGA ) module , taking advantage of the efficiency of low rank matrix-vector multiplication, that improves the algorithmic alignment (Xu et al., 2019b) of GNNs with the 2-folklore Weisfeiler-Lehman ( FWL) algorithm; 2-FWL is a graph isomorphism algorithm that is strictly more powerful than vertex coloring. Concretely, we: (i) formulate 2-FWL using polynomial kernels ;
After revision:  In an effort to improve generalization of GNNs we suggest the Low-Rank Global Attention (LRGA ) module , taking advantage of the efficiency of low rank matrix-vector multiplication, that improves the algorithmic alignment (Xu et al., 2019b) of GNNs with the 2-folklore Weisfeiler-Lehman ( 2-FWL) algorithm. In more detail we: (i) formulate 2-FWL using polynomial kernels ;