<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/15299" mdate="2007-08-24 00:00:00"> <author>Matthias Dehmer and Frank Emmert Streib and Alexander Mehler and J眉rgen Kilian and Max M眉hlhauser</author> <title>Application of a Similarity Measure for Graphs to Webbased Document Structures</title> <pages>361 - 365</pages> <year>2007</year> <volume>1</volume> <number>8</number> <journal>International Journal of Mathematical and Computational Sciences</journal> <ee>https://publications.waset.org/pdf/15299</ee> <url>https://publications.waset.org/vol/8</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>Due to the tremendous amount of information provided by the World Wide Web (WWW) developing methods for mining the structure of webbased documents is of considerable interest. In this paper we present a similarity measure for graphs representing webbased hypertext structures. Our similarity measure is mainly based on a novel representation of a graph as linear integer strings, whose components represent structural properties of the graph. The similarity of two graphs is then defined as the optimal alignment of the underlying property strings. In this paper we apply the well known technique of sequence alignments for solving a novel and challenging problem Measuring the structural similarity of generalized trees. In other words We first transform our graphs considered as high dimensional objects in linear structures. Then we derive similarity values from the alignments of the property strings in order to measure the structural similarity of generalized trees. Hence, we transform a graph similarity problem to a string similarity problem for developing a efficient graph similarity measure. We demonstrate that our similarity measure captures important structural information by applying it to two different test sets consisting of graphs representing webbased document structures.</abstract> <index>Open Science Index 8, 2007</index> </article>