Graph embedding in vector spaces by node attribute statistics

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• 作者：Jaume Gibert^a ; ^{jgibert@cvc.uab.es}  ; [Author Vitae] ; Ernest Valveny^a  ; [Author Vitae] ; Horst Bunke^b  ; [Author Vitae]
• 关键词：Structural pattern recognition ; Graph embedding ; Data clustering ; Graph classification
• 刊名：Pattern Recognition
• 出版年：2012
• 期刊代码：100_00313203
• 类别：cp
• 出版时间：September, 2012
• 卷：45
• 期：9
• 页码：3072-3083
• 文件大小：418 K

摘要

Graph-based representations are of broad use and applicability in pattern recognition. They exhibit, however, a major drawback with regards to the processing tools that are available in their domain. Graph embedding into vector spaces is a growing field among the structural pattern recognition community which aims at providing a feature vector representation for every graph, and thus enables classical statistical learning machinery to be used on graph-based input patterns. In this work, we propose a novel embedding methodology for graphs with continuous node attributes and unattributed edges. The approach presented in this paper is based on statistics of the node labels and the edges between them, based on their similarity to a set of representatives. We specifically deal with an important issue of this methodology, namely, the selection of a suitable set of representatives. In an experimental evaluation, we empirically show the advantages of this novel approach in the context of different classification problems using several databases of graphs.