边形图的边缘中心嵌入:在散散化条件下的属性和稳定性
Ahmed Begga1, Francisco Escolano Ruiz1, Miguel Ángel Lozano1
1Department of Computer Science and Artificial Intelligence, University of Alicante, 03690 Alicante, Spain.
Entropy (Basel, Switzerland)
|March 28, 2025
概括
本研究引入了以边缘为中心的图形嵌入方法,优于以节点为中心的分类和集群方法. 它利用线图和线性定理来增强链接挖掘和节点表示.
科学领域:
- 图形理论和网络分析.
- 机器学习 机器学习
- 数据挖掘 数据挖掘
背景情况:
- 传统的图形嵌入方法主要集中在节点表示上.
- 现有的方法经常从节点相似性间接推断边缘信息.
- 有需要的方法直接捕捉边缘和更高阶实体关系在指向图 (二图).
研究的目的:
- 定义和描述边缘和更高阶实体嵌入在二位图中.
- 开发一种以边缘为中心的方法,将这些嵌入与节点嵌入联系起来.
- 为了提高链接挖掘,节点分类和集群任务的性能.
主要方法:
- 嵌入线位图及其代版本.
- 利用等级属性来表达边缘/路径相似性作为节点相似性的线性组合.
- 实现用于可扩展性和使用node2vec类嵌入式和图形神经网络 (GNN) 评估性能的digraph sparsification.
主要成果:
- 基于嵌入线位图的拟议的以边缘为中心的方法,比以节点为中心的方法表现出更高的性能.
- 建立了"线性定理",表明边嵌入过渡矩阵是节点嵌入矩阵的线性组合.
- 图形散射证明了有效的可扩展性,在增加的散射水平下保持稳定的性能.
结论:
- 来自直线二图的以边为中心的嵌入式为分析定向图提供了一个强大的替代方案.
- 这种方法通过直接建模边缘关系来增强链接发现,节点分类和集群.
- 该方法具有可扩展性和适应性,有望改善各种基于图形的机器学习任务.
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