Results 1 
9 of
9
Entropy Bounds for Hierarchical Molecular Networks
 PLoS ONE
, 2008
"... In this paper we derive entropy bounds for hierarchical networks. More precisely, starting from a recently introduced measure to determine the topological entropy of nonhierarchical networks, we provide bounds for estimating the entropy of hierarchical graphs. Apart from bounds to estimate the entr ..."
Abstract

Cited by 4 (3 self)
 Add to MetaCart
In this paper we derive entropy bounds for hierarchical networks. More precisely, starting from a recently introduced measure to determine the topological entropy of nonhierarchical networks, we provide bounds for estimating the entropy of hierarchical graphs. Apart from bounds to estimate the entropy of a single hierarchical graph, we see that the derived bounds can also be used for characterizing graph classes. Our contribution is an important extension to previous results about the entropy of nonhierarchical networks because for practical applications hierarchical networks are playing an important role in chemistry and biology. In addition to the derivation of the entropy bounds, we provide a numerical analysis for two special graph classes, rooted trees and generalized trees, and demonstrate hereby not only the computational feasibility of our method but also learn about its characteristics and interpretability with respect to data analysis. 1
Application of a similarity measure for graphs to webbased document structures
 International Conference on Data Analysis ICA 2005, in conjuction with the 7th World Enformatika Conference, Budapest/Hungary
"... 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 simila ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
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.
A systems biology approach for the classification of dna microarray data
 in Proceedings of ICANN 2005, Poland/Torun
, 2006
"... Abstract. In this paper we present a binary graph classifier (BGC) which allows to classify large, unweighted, undirected graphs. The main idea of this classifier is to decompose a graph locally in generalized trees forming the tree set of a graph and to compare the tree sets of graphs by a generali ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
Abstract. In this paper we present a binary graph classifier (BGC) which allows to classify large, unweighted, undirected graphs. The main idea of this classifier is to decompose a graph locally in generalized trees forming the tree set of a graph and to compare the tree sets of graphs by a generalized treesimilarity algorithm (GTSA). We apply our BGC to networks representing coexpressed genes from DNA microarray experiments of cervical cancer and demonstrate, that different tumor stages of the disease can be distinguished on this level of description. 1
Measuring the Structural Similarity of Webbased Documents: A novel Approach
"... Abstract — Most known methods for measuring the structural similarity of document structures are based on, e.g., tag measures, path metrics and tree measures in terms of their DOMTrees. Other methods measures the similarity in the framework of the well known vector space model. In contrast to these ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
Abstract — Most known methods for measuring the structural similarity of document structures are based on, e.g., tag measures, path metrics and tree measures in terms of their DOMTrees. Other methods measures the similarity in the framework of the well known vector space model. In contrast to these we present a new approach to measuring the structural similarity of webbased documents represented by so called generalized trees which are more general than DOMTrees which represent only directed rooted trees. We will design a new similarity measure for graphs representing webbased hypertext structures. Our similarity measure is mainly based on a novel representation of a graph as strings of linear integers, 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 to solve a novel and challenging problem: Measuring the structural similarity of generalized trees. More precisely, 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. We demonstrate that our similarity measure captures important structural information by applying it to two different test sets consisting of graphs representing webbased documents.
Towards Clustering of Webbased Document
, 2005
"... Methods for organizing web data into groups in order to analyze webbased hypertext data and facilitate data availability are very important in terms of the number of documents available online. Thereby, the task of clustering webbased document structures has many applications, e.g., improving info ..."
Abstract
 Add to MetaCart
Methods for organizing web data into groups in order to analyze webbased hypertext data and facilitate data availability are very important in terms of the number of documents available online. Thereby, the task of clustering webbased document structures has many applications, e.g., improving information retrieval on the web, better understanding of user navigation behavior, improving web users requests servicing, and increasing web information accessibility. In this paper we investigate a new approach for clustering webbased hypertexts on the basis of their graph structures. The hypertexts will be represented as so called generalized trees which are more general than usual directed rooted trees, e.g., DOMTrees. As a important preprocessing step we measure the structural similarity between the generalized trees on the basis of a similarity measure d. Then, we apply agglomerative clustering to the obtained similarity matrix in order to create clusters of hypertext graph patterns representing navigation structures. In the present paper we will run our approach on a data set of hypertext structures and obtain good results in Web Structure Mining. Furthermore we outline the application of our approach in Web Usage Mining as future work.
Application of a Similarity Measure for Graphs to
 International Conference on Data Analysis ICA 2005, in conjuction with the 7th World Enformatika Conference, Budapest/Hungary
, 2005
"... 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 measur ..."
Abstract
 Add to MetaCart
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.
Preprint Number 08–40 ENTROPY BOUNDS FOR HIERARCHICAL MOLECULAR NETWORKS
"... Abstract: In this paper we derive entropy bounds for hierarchical networks. More precisely, starting from a recently introduced measure to determine the topological entropy of nonhierarchical networks, we provide bounds for estimating the entropy of hierarchical graphs. Apart from bounds to estimat ..."
Abstract
 Add to MetaCart
Abstract: In this paper we derive entropy bounds for hierarchical networks. More precisely, starting from a recently introduced measure to determine the topological entropy of nonhierarchical networks, we provide bounds for estimating the entropy of hierarchical graphs. Apart from bounds to estimate the entropy of a single hierarchical graph, we see that the derived bounds can also be used for characterizing graph classes. Our contribution is an important extension to previous results about the entropy of nonhierarchical networks because for practical applications hierarchical networks are playing an important role in chemistry and biology. In addition to the derivation of the entropy bounds, we provide a numerical analysis for two special graph classes, rooted trees and generalized trees, and demonstrate hereby not only the computational feasibility of our method but also learn about its characteristics and interpretability with respect to data analysis.
www.gldv.org Text MiningImpressum LDVForum
"... h�p://www.gldv.org/cms/vorstand.php, h�p://www.gldv.org/cms/topics.php � He�e im Jahr, halbjährlich zum ��. Mai und ��. Oktober. Preprints und redaktionelle Planungen sind über die Website der GLDV einsehbar (h�p://www.gldv.org). Unaufgefordert eingesandte Fachbeiträge werden vor Veröffentlichung vo ..."
Abstract
 Add to MetaCart
h�p://www.gldv.org/cms/vorstand.php, h�p://www.gldv.org/cms/topics.php � He�e im Jahr, halbjährlich zum ��. Mai und ��. Oktober. Preprints und redaktionelle Planungen sind über die Website der GLDV einsehbar (h�p://www.gldv.org). Unaufgefordert eingesandte Fachbeiträge werden vor Veröffentlichung von mindestens zwei ReferentInnen begutachtet. Manuskripte sollten deshalb möglichst frühzeitig eingereicht werden und bei Annahme zur Veröffentlichung in jedem Fall elektronisch und zusätzlich auf Papier übermi�elt werden. Die namentlich gezeichneten Beiträge geben ausschließlich die Meinung der AutorInnen wieder. Einreichungen sind an die Herausgeber zu übermi�eln. Für Mitglieder der GLDV ist der Bezugspreis des LDVForums im Jahresbeitrag mit eingeschlossen. Jahresabonnements können zum Preis von ��, € (inkl. Versand), Einzelexemplare zum Preis von ��, € (zzgl. Versandkosten) bei der Redaktion bestellt werden. Christoph Pfeiffer, Regensburg, mit LaTeX (pdfeTeX / MiKTeX)