关于深度图表表示学习的全面调查
Wei Ju1, Zheng Fang2, Yiyang Gu1
1School of Computer Science, National Key Laboratory for Multimedia Information Processing, Peking University, Beijing, 100871, China.
概括
本调查探讨了深度图表表示学习,突出了传统方法的局限性和深度学习的优势,特别是图表神经网络,用于编码复杂的图表数据.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 人工智能的人工智能
背景情况:
- 图表表示学习将高维图数据编码为低维向量.
- 传统方法保留了节点的近距离,但具有有限的容量和学习范式.
- 深度图形表示学习,特别是图形神经网络 (GNN),提供了先进的解决方案.
研究的目的:
- 提供当前深度图表表示学习算法的全面调查.
- 提出一种新的分类法来对最先进的文学进行分类.
- 讨论实际应用和未来的研究方向.
主要方法:
- 基本的图形表示学习组件的系统总结.
- 基于GNN架构和学习范式的现有方法的分类.
- 综述了深度图表表示学习的最新进展.
主要成果:
- 鉴定了传统图形嵌入技术的局限性.
- 突出了深度学习模型的潜力和优势,特别是GNN.
- 为了更好地理解,把现有的文学整理成一个新的分类学.
结论:
- 深度图表表示学习显著优于传统方法.
- GNN代表了从图形结构数据中学习的强大范式.
- 未来的研究应该探索该领域的新视角和具有挑战性的方向.
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