2026-09-24T06:32:24.425Z
- title:
Recurrent GraphNeural NetworkswithSet-BasedAggregation→ Recurrent Graph Neural Networks with Set-Based Aggregation
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Recurrent Graph Neural Networks with Set-Based Aggregation
Recurrent GNNs iterate message passing to convergence, and their logical characterizations to date rely on multi-set aggregation, graded (counting) logics, and halting or acceptance conditions that cannot be verified from the network's parameters. We study recurrent GNNs with set-based aggregation and identify sufficient conditions checkable from the weights for networks to compile into formulas and formulas into networks. The main result is an effective, two-directional equivalence between a class of networks and the Boolean closure of reachability and safety properties, the fragment B$Σ^{\ci
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-14T17:39:41.000Z
First collected: 2026-09-20T09:41:04.278Z. This is not the publication date.
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