Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models

• URL: http://arxiv.org/abs/2605.13540v1
• Date: Wed, 13 May 2026 13:50:03 GMT
• Title: Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models
• Abstract summary: We propose DyGFM, a Dynamic Graph Foundation Model over multiple domains based on decoupled and divergence-conditioned prompting.<n>We show that DyGFM consistently outperforms 12 state-of-the-art baselines on both node classification and link prediction tasks.
• Score: 59.8742208800447
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Dynamic graphs are ubiquitous in real-world systems, and building generalizable dynamic Graph Foundation Models has become a frontier in graph learning. However, dynamic graphs from different domains pose fundamental challenges to unified modeling, as their semantic and temporal patterns are inherently inconsistent, making the multi-domain pre-training difficult. Consequently, the widely used "pretrain-then-finetune" paradigm often suffers from severe negative knowledge transfer. To the best of our knowledge, there exists no multi-domain dynamic GFM. In this work, we propose DyGFM, a Dynamic Graph Foundation Model over multiple domains based on decoupled and divergence-conditioned prompting. To disentangle transferable semantics from the domain-specific dynamics, we introduce a dual-branch pre-training strategy with semantic-temporal decoupling. To alleviate negative transfer during domain adaptation, we further develop a cross-domain routing mechanism with divergence-aware expert selection. To enable efficient downstream fine-tuning, we design a divergence-conditioned prompt generator that injects lightweight, learnable graph prompts tailored to semantic and temporal traits. Extensive experiments on continuous dynamic graph benchmarks demonstrate that DyGFM consistently outperforms 12 state-of-the-art baselines on both node classification and link prediction tasks, achieving superior effectiveness and efficiency.

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