图形基础模型:概念,机遇和挑战
概括
图形基础模型 (GFMs) 是人工智能的新范式,在各种应用中对庞大的图形数据进行预训练. 这项工作定义了GFMs,对现有研究进行了分类,并探索了图形学习的未来方向.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形数据分析数据分析
背景情况:
- 基金会模型在AI方面取得了成功,特别是在自然语言处理方面.
- 图形机器学习正在转向深度学习方法.
- 基础模型的潜力激发了新的图形学习范式.
研究的目的:
- 介绍和定义图形基础模型 (GFMs).
- 系统地分析GFMs的特性和技术.
- 基于对图形神经网络和大型语言模型的依赖,对现有研究进行分类.
主要方法:
- 开发GFMs的概念框架.
- 文献综述和对当前研究进行分类.
- 基础技术和依赖关系的分析.
主要成果:
- 正式介绍了图形基础模型 (GFMs) 概念.
- 根据GNN和LLM集成,将GFM分为三个类别.
- 全面审查GFMs的当前状况.
结论:
- 图形机器学习 (GFMs) 是图形机器学习的一个重大进步.
- 需要进一步的研究来探索GFMs的全部潜力.
- 该领域正在迅速发展,有前途的未来研究途径.
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