改进了基于图形结构信息的跳过图表
Xiaojie Wang1, Haijun Zhao1, Huayue Chen1
1School of Computer Science, China West Normal University, Nanchong 637002, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
本研究介绍了Graph Skip-gram,这是一个新的图表表示学习方法,扩展了Skip-gram模型. 它通过结合图形结构信息来增强图形嵌入,以改进节点向量分析和特征融合.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 图形理论 图形理论
背景情况:
- 跳过图形模型在自然语言处理中广泛用于文字嵌入.
- 图表表示学习旨在学习图表中节点的低维表示.
- 现有的研究经常将Skip-gram迁移到图表学习,而没有充分探索其特定图表的应用.
研究的目的:
- 为了解决Skip-gram在图形表示学习中的不足.
- 分析词嵌入和图形嵌入之间的差异.
- 提出一个有效的图表表示学习方法,灵感来自Skip-gram.
主要方法:
- 通过案例研究分析了图形表示学习的原理.
- 开发了Skip-gram图表,这是Skip-gram模型的扩展,包含图形结构.
- 设计了一个基于节点向量相似性的新功能融合算法.
主要成果:
- 通过一个案例研究展示了图形嵌入的直观概念.
- 图表跳过图表显示了与各种算法的出色适应性.
- 对分类和链接预测任务的实验比较验证了该方法的适用性.
结论:
- Graph Skip-gram是Skip-gram用于图表表示学习的更适用的扩展.
- 拟议的特征融合算法有效地利用了节点向量相似性.
- 这项工作提供了对Skip-gram在图形领域的更深入的理解和实际应用.
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