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 without retraining. We argue that inductive cross-network generalisation is a necessary prerequisite for Graph Foundation Models (GFMs) in this domain and propose four design properties towards this goal. As a proof of concept, ts-net (TopSpreadersNetwork), trained solely on synthetic multilayer networks (MLNs), demonstrates
This paper addresses a critical limitation in current network dynamics models, driven by the increasing complexity and interconnectivity of real-world systems, and the need for more generalizable AI solutions.
Achieving inductive cross-network generalization for Graph Foundation Models could unlock significant advancements in understanding and predicting complex systems, from disease spread to financial contagion, enabling more robust interventions.
The shift from transductive to inductive models for network dynamics means that AI can be applied more broadly to unseen network structures without extensive retraining, accelerating research and practical applications.
- · AI researchers and developers
- · Public health organizations
- · Financial systems analysts
- · Cybersecurity defense
- · Traditional network modeling approaches
- · Organizations reliant on custom, single-network AI models
More accurate and adaptable AI models for predicting events like pandemics or misinformation spread across diverse networks.
Accelerated development of general-purpose AI agents capable of reasoning about and interacting with complex networked environments.
Profound improvements in societal resilience to emergent threats by enabling proactive, AI-driven interventions in dynamic systems.
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Read at arXiv cs.LG