The Boolean Power of ReLU
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Abstract (arXiv)
We prove that, on finite simple undirected graphs equipped with a single Boolean node feature, the Boolean queries expressible in Sigma-MPLang, for any collection Sigma of eventually constant activation functions and with arbitrary real coefficients, form a strict subclass of the Boolean queries expressible in ReLU-MPLang. We thereby settle a recently posed open problem: whether ReLU-MPLang is more powerful than trReLU-MPLang when it comes to Boolean queries. In particular, this implies that ReLU-GNNs are strictly more expressive than TrReLU,id-GNNs with respect to Boolean queries on Boolean-featured graphs.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 4 references that are in the map. First posted on arXiv 12 Aug 2026.
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References · 4
- 2024Decidability of Graph Neural Networks via Logical Characterizationscited by 14
- 2022On the expressive power of message-passing neural networks as global feature map transformerscited by 8
- 2013Dimension Reduction via Colour Refinementcited by 6
- 2025A Logical View of GNN-Style Computation and the Role of Activation Functionscited by 3
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