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Learning Graph Cellular Automata

NeurIPS 2021 · Graph learning › GNN architectures · approach: Theory of graph convolutional networks · 0 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Learn the transition rule with a graph neural cellular automatonVoronoi GCA at κ=0.42 · Voronoi GCA validation setaccuracy: 100%Helped
Approximate the Boids transition rule with GNCABoids system · Boids trajectoriesSampEn: 0.0369 -> 0.0302Mixed
Approximate the Boids transition rule with GNCABoids system · Boids trajectoriescorrelation dimension: 1.3125 -> 1.3017Mixed
Train for t=10 instead of using randomly sampled t∈[10,20] (ablation)GNCA target convergence · Grid2dminimum error: 1.19\cdot 10^{-6} -> 1.52\cdot 10^{-5}Hurt
Train for t=10 instead of using randomly sampled t∈[10,20] (ablation)GNCA target convergence · Bunnyminimum error: 9.76\cdot 10^{-5} -> 4.19\cdot 10^{-5}Helped
Train for t=10 instead of using randomly sampled t∈[10,20] (ablation)GNCA target convergence · Minnesotaminimum error: 1.29\cdot 10^{-6} -> 1.26\cdot 10^{-6}Helped
Train for t=10 instead of using randomly sampled t∈[10,20] (ablation)GNCA target convergence · Logominimum error: 2.49\cdot 10^{-4} -> 7.35\cdot 10^{-6}Helped
Train for t=10 instead of using randomly sampled t∈[10,20] (ablation)GNCA target convergence · SwissRollminimum error: 1.33\cdot 10^{-5} -> 1.09\cdot 10^{-5}Helped
Train for t=20 instead of using randomly sampled t∈[10,20] (ablation)GNCA target convergence · Grid2dminimum error: 1.19\cdot 10^{-6} -> 3.66\cdot 10^{-6}Hurt
Train for t=20 instead of randomly sampling t∈[10,20] (ablation)GNCA target convergence · Bunnyminimum error: 9.76\cdot 10^{-5} -> 2.18\cdot 10^{-4}Hurt
Train for t=20 instead of randomly sampling t∈[10,20] (ablation)GNCA target convergence · Minnesotaminimum error: 1.29\cdot 10^{-6} -> 1.72\cdot 10^{-6}Hurt
Train for t=20 instead of randomly sampling t∈[10,20] (ablation)GNCA target convergence · Logominimum error: 2.49\cdot 10^{-4} -> 3.75\cdot 10^{-5}Helped

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