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Recently, the Weisfeiler-Lehman (WL) graph isomorphism test was used to measure the expressiveness of graph neural networks (GNNs), showing that the neighborhood aggregation GNNs were at most as powerful as 1-WL test in distinguishing graph structures.
Reduction of a graph to a canonical form and an algebra arising during this reduction
B. Yu. Weisfeiler and A. A. Leman · 1968
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Algorithms for square roots of graphs
Yaw-Ling Lin and Steven S Skiena · 1995
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Relational mathematics , volume 132
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Benchmark data sets for graph kernels, 2016
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Semi-supervised classification with graph convolutional networks
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Dropedge: Towards deep graph convolutional networks on node classification
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