Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings

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How researchers see this paper

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GemNet: Universal Directional Graph Neural Networks for Molecules (2021, background): “Kondor et al. [37], Maron et al. [43], Morris et al. [45, 46] then investigated higher-order representations to circumvent this issue.”
UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks (2021, methodology): “, 2018a] and many provably more powerful GNNs mimicking higherorder-WL test have been presented [Morris et al., 2019a; Morris et al., 2019b; Li et al., 2020b].”
Long Range Graph Benchmark (2022, unknown): “…based GNNs [22] with respect to the 1-Weisfeiler Leman (WL) isomorphism test [60, 62, 46] and the information oversquashing [2], there has been several developments of GNN and Graph Transformer architectures which have strictly greater representation power than 1-WL [7, 3, 46, 45, 35, 34, 65].”
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks (2021, methodology): “The deep theoretical link between the WL graph isomorphism test and message passing GNNs are well known (Xu et al., 2019b).”