  Relational pooling is a framework for building more expressive and
permutation-invariant graph neural networks. However, there is limited
understanding of the exact enhancement in the expressivity of RP and its
connection with the Weisfeiler Lehman hierarchy. Starting from RP, we propose
to explicitly assign labels to nodes as additional features to improve
expressive power of message passing neural networks. The method is then
extended to higher dimensional WL, leading to a novel $k,l$-WL algorithm, a
more general framework than $k$-WL. Theoretically, we analyze the expressivity
of $k,l$-WL with respect to $k$ and $l$ and unifies it with a great number of
subgraph GNNs. Complexity reduction methods are also systematically discussed
to build powerful and practical $k,l$-GNN instances. We theoretically and
experimentally prove that our method is universally compatible and capable of
improving the expressivity of any base GNN model. Our $k,l$-GNNs achieve
superior performance on many synthetic and real-world datasets, which verifies
the effectiveness of our framework.
