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首页> 外文会议>ICMLA 2012;International Conference on Machine Learning and Applications >A Comparison of Graph Embedding Methods for Vertex Nomination
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A Comparison of Graph Embedding Methods for Vertex Nomination

机译：顶点指定的图形嵌入方法比较

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摘要

Given an attributed graph representation of data, vertex nomination works to find the group of vertices which are of interest, e.g., those vertices whose attributes are different from others', or the connection among those vertices are more frequent. In this paper we present an algorithm to estimate the power of nominating these interesting vertices. This algorithm is based on Wilcoxon rank sum test. It requires to embed graph vertices into a low dimensional space. Two graph embedding methods, adjacency spectral embedding and multidimensional scaling composed with canonical correlation analysis are employed. We investigate a case where two graphs are available for modeling the same objects in different spaces, and show the effects of data fusion on vertex nomination power.
机译：给定数据的属性图表示形式，顶点提名会找到感兴趣的一组顶点，例如属性与其他属性不同的那些顶点，或者这些顶点之间的连接更频繁。在本文中，我们提出了一种算法来估计提名这些有趣顶点的能力。该算法基于Wilcoxon秩和检验。它需要将图顶点嵌入到低维空间中。采用了图谱嵌入，邻接谱嵌入和由规范相关分析构成的多维缩放两种方法。我们研究了两个图可用于在不同空间中对同一对象进行建模的情况，并显示了数据融合对顶点提名能力的影响。

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