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Recurrent Graph Neural Networks: Halting vs. Converging Expressiveness Studied

By PulseAugur Editorial · [2 sources] ·

A new paper explores the expressiveness of different Recurrent Graph Neural Network (RGNN) models, specifically focusing on converging, output-converging, and halting RGNNs. The research establishes that on undirected graphs, converging RGNNs are as expressive as graded-bisimulation-invariant halting RGNNs, while output-converging RGNNs are at least as expressive. The study introduces a "traffic-light" protocol to address the desynchronization challenge when simulating halting RGNNs with converging ones, answering an open question in the field. AI

IMPACT Clarifies theoretical expressiveness limits of RGNN variants, potentially guiding future research in graph-based AI.

RANK_REASON Academic paper analyzing theoretical properties of RGNN models.

Read on arXiv cs.AI →

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

Recurrent Graph Neural Networks: Halting vs. Converging Expressiveness Studied

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Academic paper analyzing theoretical properties of RGNN models.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Jeroen Bollen, Stijn Vansummeren ·

    On Halting vs Converging in Recurrent Graph Neural Networks

    arXiv:2604.25551v1 Announce Type: new Abstract: Recurrent Graph Neural Networks (RGNNs) extend standard GNNs by iterating message-passing until some stopping condition is met. Various RGNN models have been proposed in the literature. In this paper, we study three such models: con…

  2. arXiv cs.AI TIER_1 English(EN) · Stijn Vansummeren ·

    On Halting vs Converging in Recurrent Graph Neural Networks

    Recurrent Graph Neural Networks (RGNNs) extend standard GNNs by iterating message-passing until some stopping condition is met. Various RGNN models have been proposed in the literature. In this paper, we study three such models: converging RGNNs, where all vertex representations …

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