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News/Latent-Hysteresis Graph ODEs: Modeling Coupled Topology-Feature Evolution via Continuous Phase Transitions
arxiv
PublishedApril 29, 2026 at 4:00 AM
—neutral

Latent-Hysteresis Graph ODEs: Modeling Coupled Topology-Feature Evolution via Continuous Phase Transitions

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2604.24293v1 Announce Type: cross Abstract: Graph neural ordinary differential equations (Graph ODEs) extend graph learning from discrete message-passing layers to continuous-time representation flows. While it supports adaptive long-range propagation, we show that Graph ODEs with strictly pos

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Discussion
Mentioned models
02
  • 01
    Graph ODEs
  • 02
    Hysteresis Graph ODE (HGODE)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#graph-learning#machine-learning#artificial-intelligence

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Mentioned models
02
  • 01
    Graph ODEs
  • 02
    Hysteresis Graph ODE (HGODE)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#graph-learning#machine-learning#artificial-intelligence

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arxivWhen Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems15harxivQuery-Driven Learning for Lifted Reasoning15harxivThe Social World Model Evaluation Gap15h
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