研究人员推出了一种新颖的动态图基础模型DyGFM，旨在处理来自多个域的数据。该模型解决了不同域之间不一致的语义和时间模式带来的挑战，而这些模式常常导致现有的“预训练-微调”方法出现负面知识迁移。DyGFM采用双分支预训练策略来实现语义-时间解耦，并采用跨域路由机制来减轻适应过程中的负面迁移。实验表明，DyGFM在节点分类和链接预测任务上的表现优于12个最先进的基线模型。 AI
影响 为动态图引入了一个新的基础模型，有望提高涉及跨不同域的复杂、演化数据的任务的性能。
排序理由 发布了一篇介绍新模型的学术论文。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
Graph prompt tuning has shown great potential in graph learning by introducing trainable prompts to enhance the model performance in conventional single-domain scenarios. Recent research has extended graph prompts to improve Graph Foundation Models (GFMs) by few-shot tuning auxil…
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 …
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 …