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New framework enables Graph Foundation Models for network dynamics

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced a new framework for Graph Foundation Models (GFMs) designed to handle network dynamics across different systems. Their approach, demonstrated by a model called ts-net, shows zero-shot generalization capabilities on real-world multilayer networks without retraining. This work addresses the limitations of current transductive models and outlines key challenges for future GFM development in this area. AI

IMPACT Enables more generalizable AI models for analyzing complex networked systems like social networks or biological systems.

RANK_REASON The cluster contains an academic paper detailing a new model and framework for graph foundation models.

Read on arXiv cs.LG →

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

New framework enables Graph Foundation Models for network dynamics

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The cluster contains an academic paper detailing a new model and framework for graph foundation models.
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134 days old
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Micha{\l} Czuba, Mateusz Stolarski, Adam Pir\'og, Piotr Bielak, Piotr Br\'odka ·

    Towards Graph Foundation Models for Dynamics in Complex Networked Systems: Lessons from Super-Spreader Identification in Multilayer Networks

    arXiv:2606.08306v1 Announce Type: new Abstract: Network dynamics - including spreading, influence maximisation, and epidemic modelling - remain largely confined to the transductive paradigm, where models are trained on a single network and cannot be reused on unseen graphs withou…

  2. arXiv cs.LG TIER_1 English(EN) · Piotr Bródka ·

    Towards Graph Foundation Models for Dynamics in Complex Networked Systems: Lessons from Super-Spreader Identification in Multilayer Networks

    Network dynamics - including spreading, influence maximisation, and epidemic modelling - remain largely confined to the transductive paradigm, where models are trained on a single network and cannot be reused on unseen graphs without retraining. We argue that inductive cross-netw…

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