Short Vita

Matthias Dehmer studied Mathematics and Computer Science (1994-1998) at the University of Siegen (Germany). Until 04/2002 he worked as mathematical researcher and IT- Consultant at Alte Leipziger Lebensversicherung and Sybase Ltd. From 05/2002 - 08/2005 he was a Ph.D. student at the Computer Science Department (Thesis Advisors: Prof. Dr. M. Mühlhäuser and Prof. Dr. A. Mehler ) at Darmstadt University of Technology (Germany) where he obtained his Ph.D in 09/2005.

Research Projects

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I am currently working on the project Network Models, Governance and R&D collaboration networks (NEMO) at the Vienna University of Technology, Discrete Mathematics and Geometry in Vienna. The main goal of this project is to investigate relationships between political governance, structure and function of social collaboration networks, particularly of those networks inferred from the European Framework Programmes. Interesting questions of this project are e.g.,

  • Inference of network structures (knowledge structures) which represent networks of certain classes having typical functions
  • Investigating network classes whose instances are collaboration networks by using graph-theoretical techniques
  • Analyzing graph-theoretical quantities e.g., degree distributions, clique distributions, metrical properties, connectivity and phase transitions etc.

My main research interests are topics in information theory, applied discrete mathematics, data mining and machine learning, and bioinformatics. Current projects where I am currently involved are e.g.,

Information Processing and Transmission in Networks
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The problem of measuring the structural information content of a graph (graph entropy) is often related to the problem of finding a partitioning of the associated vertex set. Starting from such derived vertex partitions, one obtains directly a probability distribution by dividing the number of vertices in a partition by the total number of vertices (for all partitions). Hence, an entropy or information content of a graph can be obtained. Classical approaches to obtain the mentioned vertex partitionings are often based on algebraic approaches, e.g. on determining the automorphism groups of a graph. In contrast, I have recently been introduced a concept to define the entropy of a network by avoiding the problem of determining the vertex partitions. The resulting entropy measure is mainly based on using local vertex functionals obtained by calculating vertex-spheres via the Dijkstra-algorithm. It turned out the computational complexity of the entropy measure is polynomial.

Detecting Complex Diseases by Means of Networks
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The genesis of a disease is rarely triggered by single gene mutations. It is known that an eruption of a (complex) disease is often induced by multiple gene mutations and other factors, e.g., deleterious environmental influences and certain circumstances under which people live. Well known examples for complex diseases are e.g., coronary heart disease, hypertension, diabetes, various cancers, Alzheimer's disease and others. For example, DNA microarray data from complex diseases, e.g., cancer often implicate a large number of genes in the pathogenesis and, hence, such experiments can be used to investigate which mechanisms and pathways are involved. The pathways can be represented as graphs. This leads to an interesting approach for understanding and detecting complex diseases (inferred from DNA microarray) by using network models and graph-theoretical methods. Also, graph-theoretical methods can be used to investigate gene functions and for the prognosis or diagnosis of disease subtypes representing certain networks. An ultimate goal of these experiments is to find promising candidates for biomarkers that hopefully leads to better therapeutic drug targets. I am especially interested in developing novel methods for comparing large networks inferred from DNA microarray data because classical techniques for measuring the structural similarity of graphs are often inadequate. The main reason for this is the networks have to be inferred with probabilistic graph inference techniques that leads to erroneous graphs. Hence, instead of using classical methods where the graphs are mostly assumed without any uncertainty, there is a need to develop novel probabilistic graph similarity methods for processing graphs adulterated from measurement errors.

Publications

Editorships:

Series Editor (F. Emmert-Streib and M. Dehmer) of the Book Series "Advances in Computational and Systems Biology" at Peter Lang Publishing Group

Thesis:

Dehmer M.: Schrankensätze in der analytischen Theorie der Polynome, Diploma Thesis, Universität Siegen, Mai 1998

Dehmer M.: Strukturelle Analyse Web-basierter Dokumente, Ph.D. Thesis, Technische Universität Darmstadt, September 2005

Books:

Dehmer M.: Machine Learning Approach for Network Analysis: Novel Graph Classes and Classification Techniques, Wiley Publishing, 2008, accepted

Dehmer M.: Strukturelle Analyse web-basierter Dokumente, Deutscher Universitäts-Verlag, Gabler Edition Wissenschaft, Schriftenreihe: Multimedia und Telekooperation, Editors: Lehner F., Bodendorf F., Februar 2006 [mehr]

Dehmer M.: Die analytische Theorie der Polynome. Nullstellenschranken für komplexwertige Polynome, Weissensee-Verlag, Berlin, August 2004 [mehr]

Books (Edited and Co-Edited):

Emmert-Streib F., Dehmer M.: Analysis of Microarray Data: A Network-Based Approach, Wiley-VCH Publishing, 2008

Dehmer M.: Structural Analysis of Complex Networks, 2007, Birkhäuser Publishing, 2007, accepted

Dehmer M., Emmert-Streib F.: Analysis of Complex Networks: From Biology to Linguistics, Wiley-VCH Publishing, 2007, accepted

Arabnia H. R., Dehmer M., Emmert-Streib F., Qu Yang M.: Proceedings of the 2007 International Conference on Machine Learning: Models, Technologies and Applications (MLMTA’07), Las Vegas, Nevada, USA, CSREA Press, 2006

Books (Associate Editor):

Arabnia H. R. et al.: Proceedings of the 2007 International Conference on Bioinformatics and Computational Biology (BIOCOMP’07), Dehmer M., Emmert-Streib F., (Associate Editors), Las Vegas, Nevada, USA, CSREA Press, 2007

Arabnia H. R. et al.: Proceedings of the 2007 International Conference on Artificial Intelligence (ICAI’07), Dehmer M., Emmert-Streib F., (Associate Editors), Las Vegas, Nevada, USA, CSREA Press, 2007

Arabnia H. R. et al.: Proceedings of the 2007 International Conference on Scientific Computing (CSC’07), Dehmer M., Emmert-Streib F., (Associate Editors), Las Vegas, Nevada, USA, CSREA Press, 2007

Arabnia H. R. et al.: Proceedings of the 2007 International Conference on Genetic and Evolutionary Methods (GEM’07), Dehmer M., Emmert-Streib F., (Associate Editors), Las Vegas, Nevada, USA, CSREA Press, 2007

Arabnia H. R., Valafar H.: Proceedings of the 2006 International Conference on Bioinformatics & Computational Biology (BIOCOMP’06), Budak H., Dehmer M., Emmert-Streib F., Valafar F., Qu Yang M., Zabir S. (Co-Editors), Las Vegas, Nevada, USA, CSREA Press, 2006

Books (Series Editor):

Meller J., Nowak W.: Machine Learning Approaches in Bioinformatics, In: Advances of Computational and Systems Biology, Emmert-Streib F., Dehmer M., (Series Editors), Peter Lang Publishing, 2007

Papers:

Dehmer M.: Information-theoretic Concepts for the Analysis of Complex Networks, Applied Artificial Intelligence, 2008, accepted

Dehmer M.: A Novel Method for Measuring the Structural Information Content of Networks, Cybernetics and Systems, 2007, accepted

Dehmer M., Mehler A., Emmert-Streib F.: Graph-theoretical Characterizations of Generalized Trees, In Proceedings of the International Conference on Machine Learning: Models, Technologies & Applications (MLMTA'07), Las Vegas/USA, 2007

Dehmer M., Emmert-Streib F., Gesell T.: A Comparative Analysis of Multidimensional Features of Objects Resembling Sets of Graphs, Applied Mathematics and Computation, 2007, accepted

Dehmer M., Kilian J.: On Bounds for the Zeros of Univariate Polynomials, International Journal of Applied Mathematics and Computer Science, Vol. 4 (2), 2007, 118-123

Dehmer M., Emmert-Streib F.: Structural Similarity of Directed Universal Hierarchical Graphs: A low Computational Complexity Approach, Applied Mathematics and Computation, 2007, in press

Dehmer M., Emmert-Streib F.: Comparing Large Graphs Efficiently by Margins of Feature Vectors, Applied Mathematics and Computation, Vol. 188 (2), 2007, 1699-1710

Dehmer M., Mehler A.: A new Method of Measuring Similarity for a special class of directed Graphs, Tatra Mountains Mathematical Publications, 2006, accepted

Dehmer M., Emmert-Streib F., Kilian J.: A Similarity Measure for Graphs with low Computational Complexity, Applied Mathematics and Computation, Vol. 182 (1), 2006, 447–459

Dehmer M., Emmert-Streib F., Wolkenhauer O.: Perspectives of Graph Mining Techniques, Rostocker Informatik Berichte, Vol. 30 (2), 2006, 47-57

Dehmer M.: On the Location of Zeros of Complex Polynomials, Journal of Inequalities in Pure and Applied Mathematics, Vol. 7, Issue 1, 2006 [download]

Dehmer M., Emmert-Streib F., Mehler A., Kilian J.: Measuring the Structural Similarity of Web-based Documents: A novel Approach, International Journal of Computational Intelligence Vol. 3 (1), 2006, 1-7

Emmert-Streib F., Dehmer M.: Optimization Procedure for Predicting Nonlinear Time Series based on a non-Gaussian Noise Model, MICAI 2007: Advances in Artificial Intelligence, Lecture Notes in Computes Science (LNCS), Lecture Notes in Artificial Intelligence, 2007, accepted

Emmert-Streib F., Dehmer M.: Robustness in Scale-free Networks: Comparing Directed and Undirected Networks, International Journal of Modern Physics C, Vol. 19, Issue 5, 2008

Emmert-Streib F., Dehmer M.: Nonlinear Time Series Prediction based on a Power-Law Noise Model, International Journal of Modern Physics C, 2007, accepted

Emmert-Streib F., Dehmer M.: Information Theoretic Measures of UHG Graphs with Low Computational Complexity, Applied Mathematics and Computation, Vol. 190 (2), 2007, 1783-1794

Emmert-Streib F., Dehmer M.: Topological Mappings between Graphs, Trees and Generalized Trees, Applied Mathematics and Computing, Vol. 186 (2), 2007, 1326-1333

Emmert-Streib F., Dehmer M., Seidel C.: Influence of Prior Information on the Reconstruction of the Yeast Cell Cycle from Microarray Data, Proceedings of the 2006 International Conference on Bioinformatics & Computational Biology, Gene Networks: Theory and Application, Las Vegas/USA, 2006, 477-482

Emmert-Streib F., Dehmer M.: Theoretical Bounds for the Number of inferable Edges in sparse Random Networks, Proceedings of the 2006 International Conference on Bioinformatics & Computational Biology, Gene Networks: Theory and Application, Las Vegas/USA, 2006, Workshop at BIOCOMP'06, Las Vegas/USA, 2006, 472-476

Emmert-Streib F., Dehmer M.: First Studies of the Influence of Single Gene Perturbations on the Inference of Genetic Networks, VIII. International Conference on Enformatika, Systems Sciences and Engineering, Krakow/Poland, Enformatika 10, 2005, 65-69

Emmert-Streib F., Dehmer M., Bakir H.G., Mühlhäuser M.: Influence of Noise on the Inference of Dynamic Bayesian Networks from Short Time Series, VIII. International Conference on Enformatika, Systems Sciences and Engineering, Krakow/Poland, Enformatika 10, 2005, 70-74

Gleim R., Mehler A., Dehmer M., Pustylnikov O.: Aisles through the Category Forest - Utilising the Wikipedia Category System for Corpus Building in Machine Learning, Proceedings of the 3rd International Conference on Web Information Systems and Technologies (WEBIST '07), March 3-6, 2007, Barcelona, 2006, 142-149

Gleim R., Mehler A., Dehmer M.: Web Corpus Mining by Instance of Wikipedia, Proceedings of the EACL 2006 Workshop on Web as Corpus, Trento, Italy, 2006, 3-7

Emmert-Streib F., Dehmer M., Liu J., Mühlhäuser M.: Ranking Genes from DNA Microarray Data of Cervical Cancer by a local Tree Comparison, International Journal of Biomedical Science 1:1, 2005, 17-22

Dehmer M., Emmert-Streib F., Kilian J., Zulauf A.:: Towards Clustering of web-based Document Structures, VIII. International Conference on Enformatika, Systems Sciences and Engineering, Krakow/Poland, Enformatika 10, 2005, 304-310

Dehmer M., Emmert-Streib F., Mehler A., Kilian J., Mühlhäuser M.: Application of a similarity measure for graphs to web-based document structures, VI. International Conference on Enformatika, Systems Sciences and Engineering, Budapest, Hungary, October 2005, International Academy of Sciences: Enformatika 8 (2005), 77-81

Emmert-Streib F., Dehmer M., Liu J., Mühlhäuser M.: A systems approach to gene ranking from DNA Microarray Data of cervical cancer , Systems Sciences and Engineering, Budapest/Hungary, October 2005, International Academy of Sciences: Enformatika Vol. 8 (2005), 82-87

Emmert-Streib F., Dehmer M.: A Systems Biology approach for the classification of DNA Microarray Data , International Conference on Artificial Neural Networks (ICANN), Workshop on applications of statistical and machine learning in Bioinformatics, Nicolaus Copernicus University in Torun, Warsawa/Poland

Dehmer M.: Data Mining-Konzepte und graphentheoretische Methoden zur Analyse web-basierter Daten, LDV Forum, Journal of Computational Linguistics and Language Technology, 2005, 113-141

Mehler A., Gleim R., Dehmer M.: Towards structure-sensitive hypertext categorization , Proceedings of the 29-th Annual Conference of the German Classification Society, 2005

Emmert-Streib F., Dehmer M., Kilian J.: Classification of large Graphs by a local Tree Decomposition, In Proceedings of the 2005 International Conference on Data Mining (DMIN'05), Arabnia H. R., Scime A. (Editors), 2005, 200-207

Mehler A., Dehmer M., Gleim R.: Zur automatischen Klassifikation von Webgenres, Sprachtechnologie, mobile Kommunikation und linguistische Ressourcen. Beiträge zur GLDV-Tagung 2005, Fisseni B., Schmitz H. C., Schröder B., Wagner P. (Editors), Universität Bonn, Frankfurt a. M.: Peter Lang Publishing, 158-174

Dehmer M., Mehler A., Gleim R.: Aspekte der Kategorisierung von Webseiten, Jahrestagung der Gesellschaft für Informatik, Proceedings of Informatik 2004, Lecture Notes of Computer Science, Springer, 2004, 39-43 [download]

Mehler A., Dehmer M., Gleim R.: Towards Logical Hypertext Structure. A Graph-Theoretic Perspective, Proc. of I2CS'04, Lecture Notes of Computer Science, Springer, 2004[download]

Dehmer M.: Schranken für die Nullstellen komplexwertiger Polynome, Die Wurzel, Vol. 8, 2000, 182-185 [download]

Detailed Research Interests

  • Applied Graph Theory
    • Distance and Similarity Measures for Graphs
    • Graph Classification
    • General Graph Measures
    • Metrical Properties of Graphs
    • Graph Decompositions
    • Special Graph Classes and their Characterizations
    • Graph Algorithms
    • Probabilistic Methods in Graph Theory
  • Information Theory and Statistics
    • Information Processing and Transmission in Complex Networks
    • Information-theoretic Measures for Networks, e.g., Graph Entropy etc.
    • Interplay between Information Theory and Statistics
    • Information-theoretic Aspects in Machine Learning
    • Statistical Methods applied to Complex Networks
  • Machine Learning
    • Graph Mining Techniques
    • Graph Kernels
    • General Aspects of Structural Learning
    • Mathematical Aspects of Statistical Learning
    • Textclassification
  • Chemical Graph Theory
    • Molecular Descriptors
    • Graph-theoretical Problems in QSAR/QSPR
    • Topological Complexity
    • Information Content of Chemical Structures
  • Computational and Systems Biology
    • Detecting Complex Diseases
    • Structural Analysis of Biological Networks
    • Inferring Genetic Networks
    • Selecting Genes from Microarray Data

Further Interests

  • Aspects in Complex Analysis
    • Geometry of Polynomials
    • Zero Bounds for Complex and Real Polynomials
    • Entropies of Polynomials
    • Complex Approximation

Organized and Future Events

Workshop on Gene Networks: Theory and Application at BIOCOMP' 06, Las Vegas/USA, June 26-29, 2006

MLMTA'07- The 2007 International Conference on Machine Learning: Models, Technologies & Applications Las Vegas/USA, June 25-28, 2007

ITSL'08- The 2008 International Conference on Information Theory and Statistical Learning (ITSL) Las Vegas/USA, July 14-15, 2008

Contact

Matthias Dehmer
Discrete Mathematics and Geometry
Vienna University of Technology
Wiedner Hauptstrasse 8-10
A-1040 Vienna, Austria
email: matthias 'at' dehmer.org
mdehmer 'at' geometrie.tuwien.ac.at