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Graph Neural Networks Linked to Graded Modal Logic Fragments

By PulseAugur Editorial · [2 sources] ·

Researchers have established new connections between graph neural networks (GNNs) and logical formalisms by focusing on structural preservation properties. The study demonstrates that specific fragments of graded modal logic can characterize GNN classifiers preserved under embeddings, injective homomorphisms, and homomorphisms. These findings offer a way to understand the logical expressiveness of broad GNN classes independent of specific architectural choices, while also showing that corresponding GNN architectures can achieve this expressiveness. AI

RANK_REASON Academic paper published on arXiv detailing theoretical connections between GNNs and logic. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Przemys{\l}aw Andrzej Wa{\l}\k{e}ga, Bernardo Cuenca Grau ·

    Structural Preservation and the Logical Expressiveness of Graph Neural Networks

    arXiv:2606.17882v1 Announce Type: new Abstract: Bridges between graph neural networks (GNNs) and logical formalisms have been established by fixing architectural choices, such as the types of aggregation, combination, and activation functions. These choices define restricted clas…

  2. arXiv cs.AI TIER_1 English(EN) · Bernardo Cuenca Grau ·

    Structural Preservation and the Logical Expressiveness of Graph Neural Networks

    Bridges between graph neural networks (GNNs) and logical formalisms have been established by fixing architectural choices, such as the types of aggregation, combination, and activation functions. These choices define restricted classes of GNNs for which tight correspondences with…

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