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<ref name="Giraldo-SGNNs-1">{{cite journal|title=Higher-Order Sparse Convolutions In Graph Neural Networks|journal=IEEE International Conference on Acoustics, Speech and Signal Processing|date=2023|url=https://ieeexplore.ieee.org/document/10096494|last1=Giraldo|first1=H.|last2=Javed|first2=S.|last3=Mahmood|first3=A.|last4=Malliaros|first4=F.|last5=Bouwmans|first5=T.}}</ref>
and on Sobolev norm
<ref name="Giraldo-SGNNs-2">{{cite journal|title=Higher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural Networks|journal=IEEE Transactions on Signal and Information Processing Over Networks|date=2024|last1=Giraldo|first1=H.|last2=Torovic|first2=A.|last3=Einizade|first3=A.|last4=Castro-Correa|first4=J.|last5=Badiey|first5=M.|last6=Malliaros|first6=F.|last7=Bouwmans|first7=T.}}</ref>
. Thus, Graph Neural Networks (GNNs) show great promise in modeling relationships between nodes in a graph, but capturing higher-order relationships remains a challenge for large-scale networks. {{As of|2022}}, whether or not future architectures will overcome the message passing primitive is an open research question<ref name=velickovic2022 />.
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