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logical-characterization

topic1 events
papersTODAY 04:00 UTC

arXiv paper studies recurrent graph neural networks and set-based aggregation

A new arXiv preprint examines recurrent graph neural networks, which repeatedly run message passing until it converges. The authors note that existing logical characterizations of these models rely on multi-set aggregation, counting-based graded logics, and halting or acceptance conditions that cannot be verified from the network's parameters.

research/graph-neural-networks/arxiv/theory