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Recurrent Graph Neural Networks: Halting vs. Converging Expressiveness Studied

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 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

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

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 →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 · 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 · 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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