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SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks
[article]
2022
arXiv
pre-print
While (message-passing) graph neural networks have clear limitations in approximating permutation-equivariant functions over graphs or general relational data, more expressive, higher-order graph neural networks do not scale to large graphs. They either operate on k-order tensors or consider all k-node subgraphs, implying an exponential dependence on k in memory requirements, and do not adapt to the sparsity of the graph. By introducing new heuristics for the graph isomorphism problem, we
arXiv:2203.13913v3
fatcat:bazb6yobm5cfdmiicex7r43qba