Prof. Dr. habil. Matthias Dehmer

Publikationen

Theses

• Dehmer M.: Analysis of Complex Networks: Graph and Information-theoretic Methods, Cumu- lative Habilitation Thesis, TU-Wien, 2008
• Dehmer M.: Strukturelle Analyse web-basierter Dokumente, PhD Thesis, TU-Darmstadt, 2005
• Dehmer M.: Schrankensätze in der analytischen Theorie der Polynome, Diploma Thesis, UGH- Siegen, 1998
• DehmerM.:StrukturelleAnalyseWeb-basierterDokumente,GablerEditionWissenschaft,Deutscher Universitätsverlag, Editors: Lehner F., Bodendorf F., Series: Multimedia und Telekooperation, 2006
• Dehmer M.: Die analytische Theorie der Polynome. Nullstellenschranken für komplexwertige Polynome, Weissensee-Verlag, Berlin, 2004

Books (Authored, Edited and Co-edited)

• Emmert-Streib F., Moutari S., Dehmer M.: Elements of Data Science, Machine Learning, and Artificial Intelligence Using R, Springer Publishing, Cham, Switzerland, 2023
• Emmert-Streib F., Moutari S., Dehmer M.: Mathematical Foundations of Data Science Using R, De Gruyter Oldenbourg, 2020
• Dehmer M., Emmert-Streib F., Jodlbauer H.: Entrepreneurial Complexity, CRC, 2019
• Chen Z., Dehmer M., Emmert-Streib F., Shi Y.: Modern and Interdisciplinary Problems in Net- work Science: A Translational Research Perspective, CRC, 2018
• Dehmer M., Emmert-Streib F.: Frontiers in Data Science, CRC, 2017
• Shi Y., Dehmer M., Li X., Gutman I.: Graph Polynomials (Discrete Mathematics and Its Applica- tions), CRC, 2016
• Dehmer M., Shi Y., Emmert-Streib F.: Computational Network Analysis with R: Applications in Biology, Medicine and Chemistry, Wiley-Blackwell, 2016
• Dehmer M., Chen Z., Li X., Shi Y.: Mathematical Foundations and Applications of Graph Entropy, Wiley-Blackwell, 2016
• Dehmer M., Emmert-Streib F., Holzinger A., Pickl S.: Big Data of Complex Networks, CRC, 2016
• Dehmer M., Emmert-Streib F., Pickl S.: Computational Network Theory, Wiley-Blackwell, 2015
• Dehmer M., Emmert-Streib F.: Quantitative Graph Theory: Mathematical Foundations and Ap- plications, CRC Press, 2014
• DehmerM.,MowshowitzA.,Emmert-StreibF.:AdvancesinNetworkComplexity,Wiley-Blackwell, 2013
• Emmert-Streib F., Dehmer M.: Statistical Diagnostics for Cancer: Analyzing High-Dimensional Data, Wiley-Blackwell, 2013
• DehmerM.,VarmuzaK.,BonchevD.:StatisticalModellingofMolecularDescriptorsinQSAR/QSPR, Wiley-Blackwell, 2012
• Dehmer M., Basak S. C.: Statistical and Machine Learning Approaches for Network Analysis, Wiley Series in Computational Statistics, Wiley, 2012
• Dehmer M., Emmert-Streib F., Mehler A.: Towards an Information Theory of Complex Networks: Statistical Methods and Applications, Birkhäuser Publishing, 2011
• Dehmer M., Emmert-Streib F., Graber A., Salvador A.: Applied Statistics for Network Biology: Methods in Systems Biology, Wiley-VCH, 2011
• Dehmer M.: Structural Analysis of Complex Networks, Birkhäuser Publishing, 2010
• Dehmer M., Emmert-Streib F.: Analysis of Complex Networks: From Biology to Linguistics, Wiley-VCH Publishing, 2009
• Dehmer M., Drmota M., Emmert-Streib F.: Proceedings of the 2008 International Conference on Information Theory and Statistical Learning (ITSL’08), CSREA Press, 2008
• Emmert-Streib F., Dehmer M.: Medical Biostatistics for Complex Diseases, Wiley-VCH Publish- ing, 2010
• Emmert-Streib F., Dehmer M.: Analysis of Microarray Data: A Network-based Approach, Wiley- VCH Publishing, 2008
• Emmert-Streib F., Dehmer M.: Information Theory and Statistical Learning, Springer, 2008
• Arabnia H. R., Dehmer M., Emmert-Streib F., Yang Q. U.: Proceedings of the 2007 International Conference on Machine Learning: Models, Technologies and Applications (MLMTA’07), CSREA Press, 2007

Books (Associate Editor)

• Arabnia H. R. et al.: Proceedings of the 2007 International Conference on Bioinformatics and Computational Biology (BIOCOMP’07), Dehmer M., (Associate Editor), CSREA Press, 2007
• Arabnia H. R. et al.: Proceedings of the 2007 International Conference on Artificial Intelligence (ICAI’07), Dehmer M., (Associate Editor), CSREA Press, 2007
• Arabnia H. R. et al.: Proceedings of the 2007 International Conference on Scientific Computing (CSC’07), Dehmer M., (Associate Editor), CSREA Press, 2007
• Arabnia H. R. et al.: Proceedings of the 2007 International Conference on Genetic and Evolu- tionary Methods (GEM’07). Dehmer M., (Associate Editor), CSREA Press, 2007
• Arabnia H. R., Valafar H.: Proceedings of the 2006 International Conference on Bioinformatics & Computational Biology, Dehmer M., (Associate Editor), Las Vegas, Nevada, USA, 2006, CSREA Press

Books (Series Editor)

• MellerJ.,NowakW.:MachineLearningApproachesinBioinformatics.Emmert-StreibF.,Dehmer M., (Series Editors). Peter Lang Publishing, 2007

Book Chapter

• Dehmer M., Kraus V., Emmert-Streib F., Pickl S.: What is Quantitative Graph Theory? In: Dehmer M., Emmert-Streib F. (Editors): Quantitative Graph Theory: Theoretical Foundations and Applications, CRC Press, 2014, 1-33
• Dehmer M., Dobrynin A. A.: The Uniqueness of Graph Invariants: Classical and Recent Re- sults. In: Gutman I. et al. (Editors): Topics in Chemical Graph Theory, Mathematical Chemistry Monographs, 2014
• Holzinger, A., Stocker, C., Dehmer, M.: Big complex biomedical data: Towards a taxonomy of data. In: Obaidat M. S., Filipe J. (Editors): Springer Communications in Computer and Informa- tion Science CCIS 455, 2014, 3-18
• Holzinger, A., Ofner, B., Dehmer, M.: Multi-touch Graph-Based Interaction for Knowledge Dis- covery on Mobile Devices: State-of-the-Art and Future Challenges. In: Holzinger, A., Jurisica, I. (Editors): Interactive Knowledge Discovery and Data Mining: State-of-the-Art and Future Chal- lenges in Biomedical Informatics, Springer Lecture Notes in Computer Science LNCS 8401, Berlin, Heidelberg: Springer, 2014, 241-254
• Preuß M., Dehmer M., Pickl S., Holzinger H.: On Terrain Coverage Optimization by Using a Network Approach for Universal Graph-Based Data Mining and Knowledge Discovery, In: Slezak D., Tan A. H., Peters J. F., Schwabe L. (Editors): Brain Informatics and Health – International Conference, BIH 2014, Warsaw, Poland, Springer Lecture Notes in Computer Science, 2014, 564-573
• Dehmer M., Sivakumar L.: On Comparability Graphs. In: Basak S. C., Restrepo G., Villaveces J. L. (Editors): Advances in Mathematical Chemistry, Bentham Publishing, 2014, in press
• Emmert-Streib F., De Matos Simoes R., Tripathi S., Dehmer M.: Overview of Public Cancer Databases, Resources, and Visualization Tools. In: Emmert-Streib F., Dehmer M. (Editors): Statistical Diagnostics for Cancer: Analyzing High-Dimensional Data, Wiley-Blackwell, 2013, 27-40
• Holzinger, A., Ofner B, Stocker, C., Valdez A. C., Schaar A. K., Ziefle M., Dehmer, M.: On Graph Entropy Measures for Knowledge Discovery from Publication Network Data. In: Cuzzocrea A., Kittl C., Simos D. E., Weippl E., Xu L. (Editors): Availability, Reliability, and Security in Information Systems and HCI (Lectures Notes in Computer Science), Springer, Vol. 8127, 2013, 354-362
• Müller L., Dehmer M., Emmert-Streib F.: Comparing Biological Networks: A Survey on Graph Classifying Techniques. In: Csukás, B., Prokop A.: Systems Biology: Integrative Biology and Simulation Tools, Springer, Vol. 1, 2013, 43-63
• MüllerL.,DehmerM.,Emmert-StreibF.:Network-basedMethodsforComputationalDiagnostics by Means of R. In: Trajanoski Z. (Editor): Computational Medicine, Springer, 2012, 185-197
• Dehmer M., Varmuza K.: On Aspects of the Degeneracy of Topological Indices. In: Enach- escu F., Filip F. G., Iantovics B. (Editors): Advanced Computational Technologies, Romanian Academy Press, 2012, 99-107
• Dehmer M., Mowshowitz A.: On Measuring the Complexity of Sets of Graphs Using Graph Entropy. In: Enachescu F., Filip F. G., Iantovics B. (Editors): Advanced Computational Technolo- gies, Romanian Academy Press, 2012, 176-184
• Dehmer M., Sivakumar L., Varmuza K.: On Distance-Based Entropy Measures. In: Gutman I., Furtula B. (Editors): Distance in Molecular Graphs – Theory, Mathematical Chemistry Mono- graphs, 2011, 123-138
• Dehmer M., Emmert-Streib F., Tsoy R. Y., Varmuza K.: Quantifying Structural Complexity of Graphs: Information Measures in Mathematical Chemistry. In: Putz M. (Editor): Quantum Fron- tiers of Atoms and Molecules in Physics, Chemistry, and Biology, Nova Publishing, 2011, 479- 497
• Gutman I., Zhang Y., Dehmer M., Ilic ́ A.: Altenburg, Wiener, and Hosoya Polynomials. In: Gutman I., Furtula B. (Editors): Distance in Molecular Graphs – Theory, Mathematical Chemistry Monographs, 2011, 49-70
• Dehmer M., Graber A.: Recent Developments on Information-theoretic Descriptors to Analyze Networks. In: Gutman I., Furtula B. (Editors): Novel Topological Indices in Mathematical Chem- istry, Mathematical Chemistry Monographs, 2010, 21-38
• Dehmer M., Emmert-Streib F.: Mining Graph Patterns in Web-based Systems: A Conceptual View. In: Mehler A., Sharoff S., Rehm G., Santini M. (Editors): Genres on the Web: Computa- tional Models and Empirical Studies, Springer, 2010, 237-253
• Dehmer M., Emmert-Streib F.: Detecting Pathological Pathways of a Complex Disease by a Comparative Analysis of Networks. In: Emmert-Streib F., Dehmer M. (Editors): Analysis of Microarray Data: A Network-based Approach, Wiley-VCH Publishing, 2008, 285-303

Journal Publications

• Emmert-Streib F., Tripathi S., Dehmer M.: Human team behavior and predictability in the mas- sively multiplayer online game WOT Blitz, ACM Transactions on the Web, Vol. 18 (1), 2023, 1-27
• Ghorbani M., Alidehi-Ravandi R., Dehmer M., Emmert-Streib F.: A Study of Roots of a Certain Class of Counting Polynomials, Mathematics, Vol. 11 (13), 2876, 2023
• Emmert-Streib F., Tripathi S., Dehmer M.: Analyzing the scholarly literature of digital twin re- search: Trends, topics and structure, IEEE Access, Vol. 11, 2023
• Ghorbani M., Alidehi-Ravandi R., Dehmer M., Emmert-Streib F.: A Study of Roots of a Certain Class of Counting Polynomials, Mathematics, Vol. 11 (13), 2876, 2023
• Lotfi A., Mowshowitz A., Dehmer M., A Note on Eigenvalues and Asymmetric Graphs, Axioms, Vol. 12 (6), 510, 2023
• Brezovnik S., Dehmer M., Tratnik N., Žigert-Pleteršek P.: Szeged and Mostar root-indices of graphs, Applied Mathematics and Computation, Vol. 442, 127736, 2023
• Cai J., Li W., Cai W., Dehmer M.: List injective coloring of planar graphs, Applied Mathematics and Computation, Vol. 439, 127631, 2023
• Ghorbani M., Hakimi-Nezhaad M., Dehmer M.: Novel results on partial hosoya polynomials: An application in chemistry, Applied Mathematics and Computation, Vol. 433, 127379, 2022
• Brezovnik S., Dehmer M., Tratnik N., Žigert-Pleteršek P.: Szeged-like entropies of graphs, Ap- plied Mathematics and Computation, Vol. 431, 127325, 2022
• Emmert-Streib M., Dehmer M.: Taxonomy of machine learning paradigms: A data-centric per- spective, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery Vol. 12 (5), 2022
• Ma Y., Dehmer M., Künzi U. M., Tripathi S., Ghorbani M., Tao J., Emmert-Streib F.: The useful- ness of topological indices, Information Sciences, Vol. 606, 143-151, 2022
• Zhu H., Sun Q., Tao J., Chen Z., Dehmer M., Xie G.: Flexible modeling of parafoil delivery system in wind environments, Communications in Nonlinear Science and Numerical Simulation, Vol. 108, 106210, 2022
• Perera N., Nguyen TTL., Dehmer M., Emmert-Streib F.: Comparison of text mining models for food and dietary constituent named-entity recognition, Machine Learning and Knowledge Extraction, Vol. 4 (1), 2022, 254-275
• Sun Q., Yu L., Zheng Y., Tao J., Sun H., Sun M., Dehmer M., Chen Z.: Trajectory tracking control of powered parafoil system based on sliding mode control in a complex environment, Aerospace Science and Technology, Vol. 122, 107406, 2022
• Bashath S., Perera N., Tripathi S., Manjang K., Dehmer M., Emmert-Streib.: A data-centric review of deep transfer learning with applications to text data, Information Sciences, Vol. 585, 498-528, 20224
• Holzinger A., Dehmer M., Emmert-Streib F., Cucchiara R., Augenstein I., Del Ser J., Samek W., Jurisica I., Diaz-Rodriguez N.: Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence, Information Fusion, Vol. 79, 263-278, 2022
• Li J., Dang J., Zhang J., Chen Z., Dehmer M.: Degree of satisfaction-based adaptive interaction in spatial Prisoner’s dilemma, Nonlinear Dynamics, Vol. 107 (3), 3143-3154, 2022
• Zhou Y., Zheng Y., Tao J., Sun M., Sun Q., Dehmer M., Chen Z.: Servo Health Monitoring Based on Feature Learning via Deep Neural Network, IEEE Access, Vol. 9, 160887-160896, 2021
• Ma Y., Dehmer M., Künzi U. M., Mowshowitz A., Tripathi S., Ghorbani M., Emmert-Streib F.: Relationships between symmetry-based graph measures, Information Sciences, Vol. 581, 291- 303, 2021
• Maddah S., Ghorbani M., Dehmer M.: New results of identifying codes in product graphs, Ap- plied Mathematics and Computation, Vol. 410, 126438, 2021
• Ghorbani M., Dehmer M., Lotfi A., Amraei N., Mowshowitz A., Emmert-Streib F.: On the rela- tionship between PageRank and automorphisms of a graph, Information Sciences, Vol. 579, 401-417, 2021
• Ilic A., Ghorbani M., Azizi S., Dehmer M.: On conjectures of network distance measures by using graph spectra, Discrete Applied Mathematics, Vol. 302, 248-255, 2021
• Guo H., Yin Q., Xia C., Dehmer M.: Impact of information diffusion on epidemic spreading in partially mapping two-layered time-varying networks, Nonlinear Dynamics, Vol. 105 (4), 3819- 3833, 2021
• Zheng Y., Huang Z., Tao J., Sun H., Sun Q., Sun M., Dehmer M., Chen Z.: A novel chaotic fractional-order beetle swarm optimization algorithm and its application for load-frequency active disturbance rejection control, IEEE Transactions on Circuits and Systems II: Express Briefs, 2021
• Zhu H., Sun Q., Tao J., Tan P., Chen Z., Dehmer M., Xie G.: Fluid-Structure Interaction Simu- lation and Accurate Dynamic Modeling of Parachute Warhead System Based on Impact Point Prediction, IEEE Access, Vol. 9, 104418-104428, 2021
• Zheng Y., Huang Z., Tao J., Sun H., Sun Q., Dehmer M., Sun M., Chen Z.: Power system load frequency active disturbance rejection control via reinforcement learning-based memetic particle swarm optimization, IEEE Access, Vol. 9, 116194-116206, 2021
• Zhuang H., Sun Q., Chen Z., Dehmer M.: Sliding Mode Robust Control for Maximum Allow- able Vertical Tail Damage to Aircraft Based on Linear Matrix Inequality, Journal of Aerospace Engineering, Vol. 34 (4), 05021001, 2021
• Tripathi S., Muhr D., Brunner M., Jodlbauer H., Dehmer M., Emmert-Streib F.: Ensuring the Ro- bustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manu- facturing, Frontiers in Artificial Intelligence, Vol. 22 (3), 2021
• Ghorbani M., Dehmer M.: On the Roots of the Modified Orbit Polynomial of a Graph, Symmetry, Vol. 13 (6), 972, 2021
• Ghorbani M., Dehmer M.: Network Analyzing by the Aid of Orbit Polynomial, Symmetry, Vol. 13 (5), 801, 2021
• Emmert-StreibF.,DehmerM.:Data-drivencomputationalsocialnetworkscience:Predictiveand inferential models for Web-enabled scientific discoveries, Frontiers in big Data, Vol. 4, 591749, 2021
• Ghorbani M., Jalali-Rad M., Dehmer M.: Orbit polynomial of graphs versus polynomial with integer coefficients, Symmetry, Vol. 13 (4), 710, 20215
• Manjang K., Tripathi S., Yli-Harja O., Dehmer M., Glazko G., Emmert-Streib F.: Prognostic gene expression signatures of breast cancer are lacking a sensible biological meaning, Scientific reports, Vol. 11, 156, 1-18, 2021
• Emmert-Streib F., Manjang K., Dehmer M., Yli-Harja O., Auvinen A.: Are There Limits in Ex- plainability of Prognostic Biomarkers? Scrutinizing Biological Utility of Established Signatures, Cancers, Vol. 13 (20), 5087, 2021
• Zheng Y., Tao J., Sun H., Sun Q., Chen Z., Dehmer M., Zhou Q.: Load frequency active distur- bance rejection control for multi-source power system based on soft actor-critic, Energies, Vol. 14 (16), 4804, 2021
• Manjang K., Yli-Harja O., Dehmer M., Emmert-Streib F.: Limitations of explainability for estab- lished prognostic biomarkers of prostate cancer, Frontiers in Genetics, Vol. 12, 649429, 2021
• Zhang Q., Tao J., Sun Q., Zeng X., Dehmer M., Zhou Q.: A Fall Posture Classification and Recognition Method Based on Wavelet Packet Transform and Support Vector Machine, Applied Sciences, Vol. 11 (11), 5030, 2021
• Hu B., Dehmer M., Emmert-Streib F., Zhang B.: Analysis of the real number of infected people by COVID-19: A system dynamics approach, PLOS One, Vol. 16 (3), 2021, e0245728
• Cheng T., Dehmer M., Emmert-Streib F., Li Y., Liu W., Properties of Commuting Graphs over Semidihedral Groups, Symmetry, Vol. 13 (1), 2021
• Dehmer M., Chen Z., Emmert-Streib F., Mowshowitz A., Varmuza K., Feng L., Jodlbauer H., Shi Y., Tao J.: The Orbit-Polynomial: A Novel Measure of Symmetry in Networks, Vol. 8, 2020, 47619-47639
• Dehmer M., Emmert-Streib F., Mowshowitz M., Ilic ́ A., Chen Z., Yu G., Feng L., Ghorbani M., Varmuza K., Tao J.: Relations and bounds for the zeros of graph polynomials using vertex orbits, Applied Mathematics and Computation Vol. 380, 125239, 2020
• Nadjafi-Arani M.J., Mirzargar M., Emmert-Streib F., Dehmer M.: Partition and Colored Distances in Graphs Induced to Subsets of Vertices and Some of Its Applications, Symmetry, Vol. 12 (12), 2020
• Manjang K., Tripathi S., Yli-Harja O., Dehmer M., Emmert-Streib F.: Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance Scientific reports, Vol. 10 (1), 1-16, 2020
• Ghorbani M., Dehmer M., Emmert-Streib F.: On the Degeneracy of the Orbit Polynomial and Related Graph Polynomials Symmetry, Vol. 12 (10), 2020
• Ghorbani M., Hakimi-Nezhaad M., Dehmer M., Li X.: Analysis of the Graovac-Pisanski Index of Some Polyhedral Graphs Based on Their Symmetry Group Symmetry, Vol. 12 (9), 2020
• Azemati H., Jam F., Ghorbani M., Dehmer M., Ebrahimpour R., Ghanbaran A., Emmert-Streib F.: The Role of Symmetry in the Aesthetics of Residential Building Facades Using Cognitive Science Methods Symmetry, Vol. 12 (9), 2020
• Ghorbani M., Dehmer M., Emmert-Streib F.: Properties of Entropy-Based Topological Measures of Fullerenes, Mathematics Vol. 8 (5), 740, 2020
• Wan P., Chen X., Tu J., Dehmer M., Zhang S., Emmert-Streib F.: On graph entropy measures based on the number of independent sets and matchings, Information Sciences Vol. 516, 491- 504, 2020
• Ghorbani M., Dehmer M., Cao S., Feng L., Tao J., Emmert-Streib F.: On the Zeros of the Partial Hosoya Polynomial of Graphs, Information Sciences, Vol. 524, 199-215, 20206
• Li J., Park J.H., Zhang J., Chen Z., Dehmer M.: The networked cooperative dynamics of adjust- ing signal strength based on information quantity, Nonlinear Dynamics, 1=17, 2020
• Ghorbani M., Dehmer M., Rahmani S., Rajabi-Parsa M.: A Survey on Symmetry Group of Poly- hedral Graphs, Symmetry, Vol. 12 (3), 2020,
• Emmert-Streib F., Yang Z., Feng L., Tripathi S., Dehmer M.: An Introductory Review of Deep Learning for Prediction Models With Big Data, Frontiers in Artificial Intelligence, Vol. 3 (4), 2020
• Kong M., Zhang Y., Xu D., Chen Z., Dehmer M., FCTP-WSRC: Protein-Protein Interactions Prediction via Weighted Sparse Representation Based Classification, Frontiers in genetics, Vol. 11, 2020
• Yang Z., Dehmer M., Yli-Harja O., Emmert-Streib F.: Combining deep learning with token selec- tion for patient phenotyping from electronic health records, Scientific Reports, Vol. 10 (1), 1-18, 2020
• Gao H., Tao J., Dehmer M., Emmert-Streib F., Sun Q., Chen Z., Xie G., Zhou Q.: In-flight Wind Field Identification and Prediction of Parafoil Systems, Applied Sciences, Vol. 10 (6), 2020
• Dehmer M., Chen Z., Emmert-Streib F., Tripathi S., Mowshowitz A., Levitchi A., Feng L., Shi Y., Tao J.: Measuring the complexity of directed graphs: A polynomial-based approach, PLoS ONE, Vol. 14 (11), 2019, e0223745
• Azam F., Musa A., Dehmer M., Yli-Harja O. P., Emmert-Streib F.: Global Genetics Research in Prostate Cancer: A Text Mining and Computational Network Theory Approach, Frontiers in Genetics, Vol. 10 (70), 2019
• Dehmer M., Chen Z., Emmert-Streib F., Mowshowitz A., Shi Y., Shailesh T., Zhang Y.: Towards Detecting Structural Branching and Cyclicity in Graphs: A Polynomial-based Approach, Infor- mation Science, Vol. 471, 19-28, 2019
• Dehmer. M., Chen Z., Mowshowitz A., Jodlbauer H., Emmert-Streib F., Shi Y., Tripathi S., Xia C.: On the Degeneracy of the Randic ́ Entropy and Related Graph Measures, Information Sciences, Vol. 501, 2019, 680-687
• Dehmer M., Chen Z., Shi Y., Zhang Y., Tripathi S., Ghorbani M., Mowshowitz A., Emmert-Streib F.: On efficient network similarity measures, Applied Mathematics and Computation, Vol. 362, 2019, 124521
• Emmert-Streib F., Dehmer M.: Evaluation of Regression Models: Model Assessment, Model Selection and Generalization Error, Machine Learning and Knowledge Extraction Vol. 1 (1), 2019, 521-551
• Emmert-Streib F., Yli-Harja O. P., Dehmer M.: Utilizing Social Media Data for Psychoanalysis to Study Human Personality, Frontiers in Psychology, Vol. 10 (2596), 2019
• Emmert-Streib F., Dehmer M.: Introduction to Survival Analysis in Practice, Machine Learning and Knowledge Extraction Vol. 1 (3), 2019, 1013-1038
• Emmert-Streib F., Dehmer M.: Understanding Statistical Hypothesis Testing: The Logic of Sta- tistical Inference, Machine Learning and Knowledge Extraction Vol. 1 (3), 2019, 945-961
• Liu W., Ban J., Feng F., Cheng T., Emmert-Streib F., Dehmer M.: The Maximum Hosoya Index of Unicyclic Graphs with Diameter at Most Four, Symmetry Vol. 11 (8), 2019, 1034
• Moore D., Simoes R. M., Dehmer M., Emmert-Streib F.: Prostate Cancer Gene Regulatory Network Inferred from RNA-Seq Data, Current Genomics, Vol. 20(1), 2019, 38-48
• Tao J., Du L., Dehmer M., Wen Y. Q., Xie G. M., Zhou, Q.: Path following control for towing system of cylindrical drilling platform in presence of disturbances and uncertainties, ISA Trans- actions, 20197
• Tao J., Dehmer M., Xie G. M., Zhou, Q.: A generalized predictive control-based path following method for parafoil systems in wind environments, IEEE Access, Vol. (7), 2019, 42586-42595
• Yu G., Dehmer M., Emmert-Streib F., Jodlbauer H.: Hermitian Normalized Laplacian Matrix for Directed Networks, Information Sciences, Vol. 495, 175-184, 2019
• Dehmer M., Pickl S., Shi Y., Yu G.: New inequalities for network distance measures by using graph spectra, Discrete Applied Mathematics, Vol. 252, 2019, 17-27
• Mowshowitz A., Dehmer, M., Emmert-Streib F.: A Note on Graphs with Prescribed Orbit Struc- ture, Entropy, Vol. 21 (11), 2019, 1118
• Ghorbani M., Dehmer M., Mowshowitz A., Tao J., Emmert-Streib F.: The Hosoya entropy of graphs revisited, Symmetry, Vol. 11 (8), 2019, 1013
• Ghorbani M., Dehmer M., Zangi S., Mowshowitz A., Emmert-Streib F.: A Note on Distance- Based Entropy of Dendrimers, Axioms, Vol. 8 (3), 2019
• Ghorbani M., Rajabi-Parsa M., Dehmer M., Mowshowitz A., Emmert-Streib F.: On Properties of Distance-based Entropies on Fullerene Graphs, Entropy, Vol. 21 (482), 2019
• Smolander J., Stupnikov A., Glazko G., Dehmer M., Emmert-Streib F.: Comparing biological information contained in mRNA and non-coding RNAs for classification of lung cancer patients, BMC Cancer Vol. 19 (1), 2019, 1176
• Emmert-Streib F., Dehmer M.: High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection, Machine Learning & Knowledge Extraction, Vol. 1 (1), 2019, 359-383
• Emmert-Streib F., Dehmer M.: Defining Data Science by a Data-Driven Quantification of the Community, Machine Learning & Knowledge Extraction, Vol. 1 (1), 2019, 235-251
• Emmert-Streib F., Dehmer M.: Large-Scale Simultaneous Inference with Hypothesis Testing: Multiple Testing Procedures in Practice, Machine Learning and Knowledge Extraction, Vol. 1 (2), 2019, 653-683
• Emmert-Streib F., Moutari S., Dehmer M.: A comprehensive survey of error measures for evalu- ating binary decision making in data science, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, e1303, 2019
• Emmert-Streib F., Dehmer M.: A machine learning perspective on Personalized Medicine: An automized, comprehensive knowledge base with ontology for pattern recognition, Machine Learn- ing and Knowledge Extraction, Vol. 1 (1), 2019 149-156
• Emmert-Streib F., Dehmer M., Yli-Harja O. P.: Ensuring Quality Standards and Reproducible Research for Data Analysis Services in Oncology: A Cooperative Service Model, Frontiers in Cell and Developmental Biology, Vol. 7 (349), 2019
• Emmert-Streib F., Shailesh Tripathi S., Dehmer M,: Constrained Covariance Matrices With a Bi- ologically Realistic Structure: Comparison of Methods for Generating High-Dimensional Gaus- sian Graphical Models, Frontiers in Applied Mathematics and Statistics, Vol. 5 (17), 2019
• Emmert-Streib F., Dehmer M.: Network Science: From Chemistry to Digital Society. Frontiers for Young Minds, Vol. 7 (49), 2019
• Emmert-Streib F., Dehmer M.: A Machine Learning Perspective on Personalized Medicine: An Automized, Comprehensive Knowledge Base with Ontology for Pattern Recognition, Machine Learning & Knowledge Extraction, Vol. 1(1), 2019, 149-156
• Emmert-Streib F., Dehmer M.: Inference of Genome-Scale Gene Regulatory Networks: Are There Differences in Biological and Clinical Validations? Machine Learning & Knowledge Ex- traction, Vol. 1 (1), 2019, 138-1488
• Ge L., Liu J., Zhang Y., Dehmer M.: Identifying anticancer peptides by using a generalized chaos game representation, Journal of Mathematical Bioligy, Vol. 78 (1-2), 2019, 441-463
• Ghorbani M., Dehmer M., Rajabi-Parsa M., Emmert-Streib F., Mowshowitz A.: Hosoya entropy of fullerene graphs, Applied Mathematics and Computation, Vol. 352, 2019, 88-98
• Ghorbani M., Taghvayi V., Dehmer M., Emmert-Streib F.: A graph-theoretic approach to con- struct desired cryptographic Boolean functions, Axioms, Vol. 8 (2), 2019
• Ghorbani M., Dehmer M., Zangi S.: On certain aspects of graph entropies of fullerenes, MATCH Communications in Mathematical and in Computer Chemistry, Vol. 81 (1), 2019, 163-174
• Ma Y., Cao S., Shi Y., Dehmer M., Xia C.: Nordhaus-Gaddum type results for graph irregularities, Applied Mathematics and Computation, Vol. 343, 2019, 268-272
• Musa A., Dehmer M., Yli-Harja O., Emmert-Streib F.: Exploiting Genomic Relations in Big Data Repositories by Graph-Based Search Methods, Machine Learning & Knowledge Extraction, Vol. 1 (1), 2019, 205-210
• Musa A., Tripathi S., Dehmer M., Emmert-Streib F.: L1000 Viewer: A Search Engine and Web Interface for the LINCS Data Repository, Frontiers in Genetics, Vol. 10 (557), 2019
• Musa A., Tripathi S., Dehmer M., Yli-Harja O., Kauffman S. A, Emmert-Streib F.: Systems phar- macogenomic Landscape of Drug similarities from LINCs data: Drug Association Networks, Scientific Reports, Vol. 9 (1), 2019, 7849
• Smolander J., Dehmer M., Emmert-Streib F.: Comparing deep belief networks with support vector machines for classifying gene expression data from complex disorders, FEBS Open Bio, Vol. 9 (7), 2019, 1232-1248
• Stevanovic ́ S., Stevanovic ́ D., Dehmer M.: On optimal and near-optimal shapes of external shading of windows in apartment buildings, PLoS ONE, Vol. 14 (2), 2019, e0212710
• Wan P., Tu J., Dehmer M., Zhang S., Emmert-Streib F.: Graph entropy based on the number of spanning forests of c-cyclic graphs, Applied Mathematics and Computation, Vol. 363, 2019, 124616
• Wan P., Chen X., Tu J., Dehmer M., Emmert-Streib F.: On Graph Entropy Measures Based on the Number of Independent Sets and Matchings, Information Sciences, 2019, in press
• Wu W., Sun Q., Sun M., Dehmer M., Chen Z.: Modeling and control of parafoils based on computational fluid dynamics, Applied Mathematical Modelling, Vol. 70, 2019, 378-401
• Xia C., Wang Z., Zheng C., Guo Q., Shi Y., Dehmer M., Chen Z.: A new coupled disease- awareness spreading model with mass media on multiplex networks, Information Sciences, Vol. 471, 2019, 185-200
• Ghorbani M., Dehmer M., Zangi S.: Graph Operations based on Using Distance-based Graph Entropies, Applied Mathematics and Computation, Vol. 333, 2018, 547-555
• Iantovics L. B., Dehmer M., Emmert-Streib F.: MetrIntSimil- An Accurate and Robust Metric for Comparison of Similarity in Intelligence of Any Number of Cooperative Multiagent Systems, Symmetry, Vol. 10 (48), 2018
• Jodlbauer H., Dehmer M., Strasser S.: A Hybrid Binomial Inverse Hypergeometric Probability Distribution: Theory and Applications, Applied Mathematics and Computation, Vol. 338, 2018, 44-54
• Emmert-Streib F., Musa A., Baltakys K., Kanniainen J., Tripathi S., Yli-Harja O., Jodlbauer H., Dehmer M.: Computational analysis of structural properties of economic and financial networks, Journal of Network Theory in Finance, Vol. 4 (3), 2018, 1-329
• Emmert-Streib F., Musa A., Tripathi S., Kandhavelu M., Dehmer M.: Harnessing the biologi- cal complexity of Big Data from LINCS Gene Expression Signatures, PLoS ONE, Vol. 13 (8), e0201937, 2018
• Emmert-Streib F., Yli-Harja O., Dehmer M.: Data analytics applications for streaming data from social media: What to predict?, Frontiers in Big Data-Data Mining and Management, Vol. 1, 2018
• Emmert-Streib F., Tripathi S., Yli-Harja O., Dehmer M.: Understanding the world economy in terms of networks: A survey of data-based network science approaches on economic networks, Frontiers in Applied Mathematics and Statistics-Mathematical Finance, Vol. 4, 2018
• Dehmer M., Emmert-Streib F.: Comments to ’Quantification of network structural dissimilarities’ published by Schieber et al., Mathematical Methods in the Applied Sciences, Vol. 41 (14), 2018, 5711-5713
• Dehmer M., Chen Z., Emmert-Streib F., Shi Y., Shailesh T., Musa A., Mowshowitz A.: Properties of Graph Distance Measures by Means of Discrete Inequalities, Applied Mathematics Modelling, Vol. 59, 2018, 739-749
• Dehmer M., Chen Z., Emmert-Streib F., Shi Y., Shailesh T.: Graph measures with high discrim- ination power revisited: A random polynomial approach, Information Science, Vol. 467, 2018, 407-414
• Lui S., Xu C., Zhang Y., Liu Y., Yu B., Liu X., Dehmer M.: Feature selection of gene expression data for Cancer classification using double RBF-kernels, BMC Bioinformatics Vol. 19 (396), 2018
• Ma Y., Cao S., Shi Y., Gutman I., Dehmer M., Furtula B.: From the connectivity index to various Randic ́-type descriptors, MATCH Commun. Math. Comput. Chem., Vol. 80 (1) 2018, 85-106
• Zheng C., Xia C., Guo Q., Dehmer M.: Interplay between SIR-based disease spreading and awareness diffusion on multiplex networks, Journal of Parallel and Distributed Computing, Vol. 115, 2018, 20-28
• Tao J., Sun Q., Liang W., Chen Z., He Y., Dehmer M.: Computational fluid dynamics based dynamic modelling of parafoil system, Applied Mathematical Modelling, Vol. 54, 2018, 136-150
• Mowshowitz A., Dehmer M.: A Calculus for Measuring the Elegance of Abstract Graphs, Applied Mathematics and Computation, Vol. 320, 2018, 142-148
• Tao J., Sun Q., Sun H., Chen Z., Dehmer M.: Dynamic Modeling and Trajectory Tracking Control of Parafoil System in Wind Environments, IEEE/ASME Transactions on Mechatronics, Vol. 22 (6), 2017, 2736-2745
• Chen Z., Dehmer M., Emmert-Streib F., Mowshowitz A., Shi Y.: Toward Measuring Network Aesthetics Based on Symmetry, Axioms, Vol. 6 (12), 2017
• Cao S., Dehmer M., Kang Z.: Network Entropies Based on Independent Sets and Matchings, Applied Mathematics and Computation, Vol. 307, 2017, 265-270
• Dehmer M., Emmert-Streib F., Shi Y.: Quantitative Graph Theory: A new branch of graph theory and network science, Information Sciences, Vol. 418-419C, 2017, 575-580
• Dehmer M., Emmert-Streib F., Hu B., Shi Y., Stefu M., Tripathi S.: Highly unique network de- scriptors based on the roots of the permanental polynomial, Information Sciences, Vol. 408, 2017, 176-181
• Emmert-StreibF.,DehmerM.,Yli-HarjaO.:LessonsfromtheHumanGenomeProject:Modesty, honesty and realism, Frontiers in Genetics-Bioinformatics and Computational Biology, Vol. 8 (184), 201710
• Li T., Dong H., Shi Y., Dehmer M.: A Comparative Analysis of New Graph Distance Measures and Graph Edit Distance, Information Sciences, Vol. 403-404, 2017, 15-21
• Musa A., Ghoraie L. S., Zhang S. D., Glazko G., Yli-Harja O., Dehmer M., Haibe-Kains B., Emmert-Streib S.: A review of connectivity map and computational approaches in pharmacoge- nomics, Briefings in Bioinformatics, 2017, 1-18
• Tripathi S., Lloyd-Price J., Ribeiro A., Yli-Harja O., Dehmer M., Emmert-Streib F.: sgnesR: An R package for simulating gene expression data from an underlying real gene network structure considering delay parameters, BMC Bioinformatics, Vol. 18 (325), 2017
• Yu G., Qu H., Dehmer M.: Principal minor version of Matrix-Tree theorem for mixed graphs, Applied Mathematics and Computation, Vol. 309, 2017, 27-30
• Xu C., Li. G., Zhang Y., Gutman I., Dehmer M.: Prediction of therapeutic peptides by incorpo- rating q-Wiener index into Chou’s general PseAAC, Journal of Biomedical Informatics, Vol. 75, 2017, 63-69
• Yu L., Zhang Y., Gutman I., Shi Y., Dehmer M.: Protein Sequence Comparison Based on Physic- ochemical Properties and Position-Feature Energy Matrix, Scientific Reports, Vol. 7 (46237), 2017
• Chen Z., Dehmer M., Shi Y., Yang H.: Sharp Upper Bounds for the Balaban Index of Bicyclic Graphs, MATCH Commun. Math. Comput. Chem., Vol. 75 (1), 2016, 105-128
• Das K. Ch., Dehmer M., A Conjecture Regarding the Extremal Values of Graph Entropy Based on Degree Powers, Entropy, Vol. 18 (183), 2016
• Das K. Ch., Dehmer M., Comparison between the zeroth-order Randic ́ index and the sum- connectivity index, Applied Mathematics and Computation, Vol. 266, 2016, 1027-1030
• Dehmer M., Mowshowitz A.: A Case Study of Cracks in the Scientific Enterprise: Reinvention of Information-Theoretic Measures for Graphs, Complexity, Vol. 21, 2016, 20-22
• Emmert-Streib M., Moutari S., Dehmer M.: The process of analyzing data is the emergent feature of data science, Frontiers in Genetics, 2016, Vol. 7 (12)
• Emmert-Streib F., Dehmer M., Shi Y.: Fifty Years of Graph Matching, Network Alignment and Network Comparison, Information Sciences, Vol. 346-347, 2016, 180-197
• Emmert-Streib M., Dehmer M., Yli-Harja O.: Against Dataism and for Data Sharing of Big Biomedical and Clinical Data with Research Parasites, Frontiers in Genetics, Vol. 7 (154), 2016
• Stupnikov A., Tripathi S., de Matos Simoes R., McArt D., Salto-Tellez M., Galina G., Dehmer M., Emmert-Streib M.: samExploreR: Exploring reproducibility and robustness of RNA-seq results based on SAM files, Bioinformatics, Vol. 32 (20), 2016
• Tripathi S., Moutari S., Dehmer M., Emmert-Streib M.: Comparison of module detection algo- rithms in protein networks and investigation of the biological meaning of predicted modules, Bioinformatics, 2016, Vol. 17 (129)
• Cao S., Dehmer M.: Degree-Based Entropies of Networks Revisited, Applied Mathematics and Computation, Vol. 261, 2015, 141-147
• Chen Z., Dehmer M., Shi Y.: Bounds for degree-based Network Entropies, Applied Mathematics and Computation, Vol. 265, 2015, 983-993
• ChenZ.,DehmerM.,Emmert-StreibF.,ShiY.:EntropyofWeightedGraphswithRandic ́Weights, Entropy, Vol. 17 (6), 2015, 3710-3723
• DehmerM.,LiX.,ShiY.:ConnectionsBetweenGeneralizedGraphEntropiesandGraphEnergy, Complexity, Vol. 21, 2015, 35-4111
• Dehmer M., Emmert-Streib F., Shi Y., Stefu M., Tripathi S.: Discrimination Power of Polynomial- based Descriptors for Graphs by Using Functional Matrices, PLoS ONE, Vol. 10 (10), 2015, e0139265
• Dehmer M., Shi Y.: A Method for Inferring Inequalities for Probability Values Applied to Complex Networks, Complexity, Vol. 21 (51), 2015, 113-115
• Dehmer M., Meyer-Nieberg S., Mihelcic G., Pickl S., Zsifkovits M.: Collaborative Risk Manage- ment for National Security and Strategic Foresight, EURO Journal on Decision Processes, Vol. 3 (3), 2015, 305-337
• Dehmer M., Moosbrugger M., Shi Y.: Encoding Structural Information Uniquely With Polynomial- based Descriptors by Employing The Randic ́ Matrix, Applied Mathematics and Computation, Vol. 268, 2015, 164-168
• Dehmer M., Emmert-Streib F., Shi Y.: Graph Distance Measures Based on Topological Indices Revisited, Applied Mathematics and Computation, Vol. 266, 2015, 623-633
• Dehmer M., Kurt Z., Emmert-Streib F., Them C., Schulc E., Hofer S.: Structural Analysis of Treatment Cycles Representing Transitions Between Nursing Organizational Units Inferred from Diabetes, PLoS ONE, Vol. 10 (6), 2015, e0127152
• Dehmer M., Varmuza K.: A Comparative Analysis of the Tanimoto Index and Graph Edit Dis- tance for Measuring the Topological Similarity of Trees, Applied Mathematics and Computation, Vol. 259, 2015, 242-250
• Dehmer M., Shi Y., Mowshowitz A.: Discrimination Power of Graph Measures based on Complex Zeros of The Partial Hosoya Polynomial, Applied Mathematics and Computation, Vol. 250 (1), 2015, 352-355
• Emmert-Streib F., Dehmer M.: Biological networks: The microscope of the 21st century, Fron- tiers in Genetics, Vol. 6 (307), 2015
• Li X., Qin Z., Wei M., Gutman I., Dehmer M.: Novel inequalities for generalized graph entropies – Graph energies and topological indices, Applied Mathematics and Computation, Vol. 259, 2015, 470-479
• Ilic ́ A., Dehmer M.: On the Distance Based Graph Entropies, Applied Mathematics and Compu- tation, Vol. 269, 2015, 647-650
• Mowshowitz A., Dehmer M.: The Hosoya entropy of a graph, Entropy, Vol. 17 (3), 2015, 1054- 1062
• Nistor, M. S., Dehmer, M., Pickl, S., Network Exploratory Analysis on Subway Transportation Systems against Complex Threats Including a Human Factors Perspective. Procedia Manufac- turing, Vol. 3, 2015, 6593-6598
• Altay G., Kurt Z., Dehmer M., Emmert-Streib F.: Netmes: Assessing gene network inference algorithms by ensemble network-based measures, Evolutionary Bioinformatics, Vol. 10, 2014, 1-9
• Cao S., Dehmer M., Shi Y.: Extremality of Degree-Based Graph Entropies, Information Sci- ences, Vol. 278, 2014, 22-33
• Chen Z., Dehmer M., Emmert-Streib F., Shi Y.: Entropy Bounds for Dendrimers, Applied Mathe- matics and Computation, Vol. 242, 2014, 462-472
• Chen Z., Dehmer M., Shi Y.: A Note on Distance-based Graph Entropies, Entropy, Vol. 16 (10), 2014, 5416-5427
• Dehmer M., Tsoy Y. R.: Numerical Evaluation and Comparison of Kalantari’s Zero Bounds for Complex Polynomials, PLoS ONE, Vol. 9 (10), 2014, e110540.12
• Dehmer M., Mowshowitz A., Shi Y.: Structural Differentiation of Graphs using Hosoya-based Indices, PLoS ONE, 2014, Vol. 9 (7), e102459
• Dehmer M., Emmert-Streib F., Grabner M.: A Computational Approach to Construct a Multivari- ate Complete Graph Invariant, Information Sciences, Vol. 260 (1), 2014, 200-208
• Dehmer M., Shi Y.: The Uniqueness of DMAX-Matrix Graph Invariants, PLoS ONE, Vol. 9 (1), 2014, e83868
• Dehmer M., Emmert-Streib F., Shi Y.: Interrelations of Graph Distance Measure Based on Topo- logical Indices, PLoS ONE, Vol. 9 (4), 2014, e94985
• Emmert-Streib F., Dehmer M., Haibe-Kains B.: Gene regulatory networks and their applica- tions: Understanding biological and medical problems in terms of networks, Frontiers in Cell and Developmental Biology, Vol. 2, Article 38, 2014
• Emmert-Streib F., Dehmer M., Haibe-Kains B.: Untangling statistical and biological models to understand network inference: The need for a genomics network ontology, Frontiers in Genetics, Vol. 5, Article 299, 2014
• Emmert-Streib F., de Matos Simoes R., Mullan P., Haibe-Kains B., Dehmer M.: The gene regu- latory network for breast cancer: Integrated regulatory landscape of cancer hallmarks, Frontiers in Genetics, Vol. 15 (5), 2014
• Emmert-Streib F., de Matos Simoes R., Glazko G., McDade S., Haibe-Kains B., Holzinger A., Dehmer M., Campbell F.: Functional and genetic analysis of the colon cancer network, BMC Bioinformatics, Vol. 15 (Suppl 6):S6, 2014
• Holzinger A., Dehmer M., Jurisica I., Knowledge Discovery and Interactive Data Mining in Bioin- formatics – State-of-the-Art, Future challenges and Research Directions, BMC Bioinformatics, Vol. 15 (Suppl 6:I1), 2014
• Kraus V., Dehmer M., Emmert-Sreib F.: Probabilistic Inequalities for Evaluating Structural Net- work Measures, Information Sciences, Vol. 288, 2014, 220-245
• Schutte M., Dehmer M.: Large-Scale Analysis of Structural Branching Measures, Journal of Mathematical Chemistry, Vol. 52 (3), 2014, 805-819
• Tripathi S., Dehmer M., Emmert-Streib F.: NetBioV: An R package for visualizing large network data in biology and medicine, Bioinformatics, Vol. 30 (19), 2014, 2834-2836
• Dander A., Müller L., Gallasch R., Pabinger S., Emmert-Streib F., Graber A., Dehmer M.: A Large-Scale Database of Molecular Descriptors using compounds from PubChem, Source Code for Biology and Medicine, Vol. 8 (22), 2013
• Dehmer M., Emmert-Streib F., Tripathi S.: Large-Scale Evaluation of Molecular Descriptors by Means of Clustering, PLoS ONE, Vol. 8 (12), 2013, e83956
• Dehmer M., Müller L., Emmert-Streib F.: Quantitative Network Measures as Biomarkers for Classifying Prostate Cancer Disease States: A Systems Approach to Diagnostic Biomarkers, PLoS ONE, Vol. 8 (11), 2013, e77602
• Dehmer M., Mowshowitz A.: The Discrimination Power of Structural SuperIndices, PLoS ONE, Vol. 8 (7), 2013, e70551
• Dehmer M., Grabner M., Mowshowitz A., Emmert-Streib F.: An Efficient Heuristic Approach to Detecting Graph Isomorphism Based on Combinations of Highly Discriminating Invariants, Advances in Computational Mathematics, Vol. 39 (2), 2013, 311-325
• Dehmer M., Hackl W. O., Emmert-Streib F., Schulc E., Them C.: Network Nursing: Connections between Nursing and Complex Network Science, International Journal of Nursing Knowledge, Vol. 24 (3), 2013, 150-15613
• Dehmer M., Grabner M.: The Discrimination Power of Molecular Identification Numbers Revis- ited, MATCH Commun. Math. Comput. Chem., Vol. 69 (3), 2013, 785-794
• de Matos Simoes R., Dehmer M., Emmert-Streib F.: B-cell lymphoma gene regulatory networks: Biological consistency among inference methods, Frontiers in Genetics, Vol. 4, Article 281, 2013
• deMatosSimoesR.,DehmerM.,Emmert-StreibF.:InterfacingcellularnetworksofS.cerevisiae and E. coli: Connecting dynamic and genetic information, BMC Genomics, Vol. 14 (324), 2013
• Emmert-Streib F., Dehmer M.: Enhancing systems medicine beyond genotype data by dynamic patient signatures: Having information and using it too, Frontiers in Bioinformatics and Compu- tational Biology, Vol. 4, Article 241, 2013
• Emmert-Streib F., Dehmer M., Lyardet F.: Learning Systems Biology: Conceptual Considera- tions Toward a Web-based Learning Platform, Education Sciences, Vol. 3 (2) 2013, 158-171
• Emmert-Streib F., Tripathi S., de Matos Simoes R., Hawwa A. F., Dehmer M.: The human dis- ease network. Opportunities for classification, diagnosis and prediction of disorders and disease genes, Systems Biomedicine, Vol. 1 (1), 2013, 1-8
• Furtula B., Gutman I., Dehmer M.: On Structure-Sensitivity of Degree-Based Topological In- dices, Applied Mathematics and Computation, Vol. 219, 2013, 8973-8978
• KrausV.,DehmerM.,SchutteM.:OnSphere-RegularGraphsandtheExtremalityofInformation- Theoretic Network Measures, MATCH Commun. Math. Comput. Chem., Vol. 70 (3), 2013, 885-900
• Varmuza K., Filzmoser P., Dehmer M.: Multivariate linear QSPR/QSAR models: Rigorous eval- uation of variable selection for PLS, Computational and Structural Biotechnology Journal, Vol. 5 (6), e201302007, 2013, 1-10
• Dehmer M., Kraus V.: On Extremal Properties of Graph Entropies, MATCH Commun. Math. Comput. Chem., Vol. 68 (3), 2012, 889-912
• Dehmer M., Tsoy Y. R.: The Quality of Zero Bounds for Complex Polynomials, PLoS ONE, Vol. 7 (7), 2012, e39537
• Dehmer M., Grabner M., Furtula B.: Structural Discrimination of Networks by Using Distance, Degree and Eigenvalue-Based Measures, PLoS ONE, Vol. 7 (7), 2012, e38564
• Dehmer M., Grabner M., Varmuza K.: Information Indices with High Discriminative Power for Graphs, PLoS ONE, Vol. 7 (2), 2012, e31214
• Dehmer M., Ilic ́ A.: Location of Zeros of Wiener and Distance Polynomials, PLoS ONE, Vol. 7 (3), 2012, e28328
• Dehmer M., Sivakumar L.: Recent Developments in Quantitative Graph Theory: Information Inequalities for Networks, PLoS ONE, Vol. 7 (2), 2012, e31395
• Dehmer M., Sivakumar L., Varmuza K.: Uniquely Discriminating Molecular Structures Using Novel Eigenvalue-based Descriptors, MATCH Commun. Math. Comput. Chem., Vol. 67 (1), 2012, 147-172
• Emmert-Streib F., de Matos Simoes R., Tripathi S., Glazko G. V., Dehmer M.: A Bayesian anal- ysis of the chromosome architecture of human disorders by integrating reductionist data, Scien- tific Reports (Nature Publishing), Vol. 2, 2012
• Emmert-Streib F., Dehmer M.: Exploring statistical and population aspects of network complex- ity, PLoS ONE 7(5), 2012, e34523
• Grabner M., Dehmer M., Varmuza K.: RMol: A Toolset for Transforming SD/Molfile structure information into R Objects, Source Code for Biology and Medicine, Vol. 7 (12), 201214
• Kusonmano K., Kugler K., Graber A., Emmert-Streib F., Dehmer M.: Effects of pooling sam- ples on the performance of classification algorithms: A comparative study, The Scientific World Journal, Vol. 12, 2012
• Mowshowitz A., Dehmer M.: Entropy and the Complexity of Graphs Revisited, Entropy, Vol. 14 (3), 2012, 559-570
• Netzer M., Kugler K., Müller L., Weinberger K., Graber A., Baumgartner C., Dehmer M.: A Network-Based Feature Selection Approach to Identify Metabolic Signatures in Disease, Journal of Theoretical Biology, Volume 310, 2012, 216-222
• Sivakumar L., Dehmer M.: Towards Information Inequalities for Generalized Graph Entropies, PLoS ONE, Vol. 7 (6), 2012, e38159
• Varmuza K., Filzmoser P., Liebmann B., Dehmer M.: Redundancy analysis for characterizing the correlation between groups of variables – Applied to molecular descriptors, Chemometrics and Intelligent Laboratory Systems, Vol. 117, 31-41, 2012
• Dehmer M.: Information Theory of Networks, Symmetry, Vol. 3 (4), 2011, 767-779
• Dehmer M.: Inclusion Radii for the Zeros of Special Polynomials, Buletinul Academiei de Stiinte a Republicii Moldova. Matematica, Vol. 3 (67), 2011, 84-90
• Dehmer M., Mowshowitz A.: Bounds on the Moduli of Polynomial Zeros, Applied Mathematics and Computation, Vol. 218 (8), 2011, 4128-4137
• Dehmer M., Mowshowitz A.: Generalized Graph Entropies, Complexity, Vol. 17 (2), 2011, 45-50
• Dehmer M., Mowshowitz A., Emmert-Streib F.: Connections between Classical and Parametric Network Entropies, PLoS ONE, Vol. 6 (1), 2011, e15733
• Dehmer M., Mowshowitz A.: A History of Graph Entropy Measures, Information Sciences, Vol. 1 (1), 2011, 57-78
• Emmert-Streib F., Dehmer M.: Networks for Systems Biology: Conceptual Connection of Data and Function, IET Systems Biology, Vol. 5 (3), 2011, 185-207
• Kugler K., Müller L., Graber A., Dehmer M.: Graph Prototyping for Co-Expression Cancer Net- works, PLoS ONE Vol. 6 (7), 2011, e22843
• Müller L., Kugler K., Graber A., Dehmer M.: A Network-Based Approach to Classify the Three Domains of Life, Biology Direct, Vol. 6 (53), 2011
• Müller L., Kugler K., Graber A., Emmert-Streib F., Dehmer M.: Structural Measures for Network Biology Using QuACN, BMC Bioinformatics, Vol. 12 (492), 2011
• Varmuza K., Dehmer M.: Classification of AMES mutagenicity from molecular descriptors – Ap- plying D-PLS regression and repeated double cross validation, Asian Chemistry Letters, 2011, accepted
• Balasubramanian R., Müller L., Kugler K., Hackl W., Pleyer L., Dehmer M., Graber A.: The Impact of Storage Effects in Biobanks on Biomarker Discovery in Systems Biology Studies, Biomarkers, Vol. 15 (8), 2010, 677-683
• Dehmer M., Barbarini N., Varmuza K., Graber A.: Novel Topological Descriptors for Analyzing Biological Networks, BMC Structural Biology, Vol. 10 (18), 2010
• Dehmer M., Müller L., Graber A.: New Polynomial-based Molecular Descriptors With Low De- generacy, PLoS ONE, Vol. 5 (7), 2010, e11393
• Dehmer M., Emmert-Streib F., Tsoy Y. R., Varmuza K.: Novel Information Measure for the Anal- ysis of Chemical Graphs (in Russian), Bulletin of the Tomsk Polytechnic University, Vol. 316 (5), 2010, 5-1115
• DehmerM.,PopovscaiaM.:TowardsStructuralNetworkAnalysis,BuletinulAcademieideStiinte a Republicii Moldova. Matematica, Vol. 1, 2010, 3-22
• Dehmer M., Mowshowitz A.: Inequalities for Entropy-Based Measures of Network Information Content, Applied Mathematics and Computation, Vol. 215 (12), 2010, 4263-4271
• Diudea M. V., Ilic ́ A., Varmuza K., Dehmer M.: Network Analysis Using a Novel Highly Discrimi- nating Topological Index, Complexity, Vol. 16 (6), 2010, 32-39
• Emmert-Streib F., Dehmer M.: Influence of the Time Scale on the Construction of Financial Networks, Complexity, PLoS ONE, Vol. 5 (9), 2010, e12884
• Emmert-Streib F., Dehmer M.: Identifying Critical Financial Networks of the DJIA: Towards a Network based Index, Complexity, Vol. 16 (1), 2010, 24-33
• Mowshowitz A., Dehmer M.: A Symmetry Index for Graphs, Symmetry: Culture and Science, Vol. 21 (4), 2010
• Molina F., Dehmer M., Perco P., Graber A., Girolami M., Spasovski G., Schanstra J. P., Vlahou A.: Systems Biology: Opening new avenues in clinical research, Nephrology Dialysis Transplan- tation, Vol 25 (4), 2015, 1015-1018
• Müller L., Kugler K., Dander A., Graber A., Dehmer M.: QuACN – An R Package for Analyzing Complex Biological Networks Quantitatively, Bioinformatics, Vol.27 (1), 2010, 140-141
• Borgert S., Dehmer M., Aitenbichler E.: On Quantitative Network Complexity Measures for Busi- ness Process Models, Acta Universitatis Apulensis, Vol. 18, 2009
• Dehmer M., Borgert S.: Information Measures to Characterize Weighted Chemical Structures, Acta Universitatis Apulensis, Vol. 18, 2009
• Dehmer M., Barbarini N., Varmuza K., Graber A.: A Large Scale Analysis of Information- Theoretic Network Complexity Measures Using Chemical Structures, PLoS ONE, Vol. 4 (12), 2009, e8057
• Dehmer M., Borgert S., Bonchev D.: Information Inequalities for Graphs, Symmetry: Culture and Science, Symmetry in Nanostructures, Vol. 19 (4), 2009
• Dehmer M., Varmuza K., Borgert S., Emmert-Streib F.: On Entropy-based Molecular Descrip- tors: Statistical Analysis of Real and Synthetic Chemical Structures, Journal of Chemical Infor- mation and Modelling, Vol. 49, 2009, 1655-1663
• Dehmer M., Borgert S.: Characterizing Classes of Structured Objects by Means of Information Inequalities, Cybernetics and Systems, Vol. 40 (3), 2009, 249-258
• Emmert-Streib F., Dehmer M.: Hierarchical coordination of periodic genes in the cell cycle of Saccharomyces cerevisiae, BMC Systems Biology, Vol. 3 (76), 2009
• Emmert-Streib F., Dehmer M.: Predicting cell cycle regulated genes by causal interactions, PLoS ONE, Vol. 4 (8), e6633, 2009
• Emmert-Streib F., Dehmer M.: Information Processing in the Transcriptional Regulatory Network of Yeast: Functional Robustness, BMC Systems Biology, Vol. 3 (35), 2009
• Emmert-Streib F., Dehmer M.: Fault Tolerance of Information Processing in Gene Networks, Physica A, Vol. 338 (4), 2009, 541-548
• Dehmer M., Emmert-Streib F: Structural Information Content of Networks: Graph Entropy based on Local Vertex Functionals, Computational Biology and Chemistry, Vol. 32, 2008, 131-138
• DehmerM.,Emmert-StreibF:TheStructuralInformationContentofChemicalNetworks,Zeitschrift für Naturforschung A, Vol. 63a, 2008, 155-15816
• Dehmer M., Borgert S., Emmert-Streib F.: Entropy Bounds for Hierarchical Molecular Networks, PLoS ONE, Vol. 3 (8), 2008, e3079
• Dehmer M.: Information-theoretic Concepts for the Analysis of Complex Networks, Applied Arti- ficial Intelligence, Vol. 22, 2008, 684-706
• Dehmer M.: Information Processing in Complex Networks: Graph Entropy and Information Functionals, Applied Mathematics and Computation, Vol. 201 (1-2), 2008, 82-94
• Dehmer M.: A Novel Method for Measuring the Structural Information Content of Networks, Cybernetics and Systems, Vol. 39 (8), 2008, 825-842
• Dehmer M., Emmert-Streib F., Gesell T.: A Comparative Analysis of Multidimensional Features of Objects Resembling Sets of Graphs, Applied Mathematics and Computation, Vol. 196 (1), 2008, 221-235
• Emmert-Streib F., Dehmer M.: Robustness in Scale-free Networks: Comparing Directed and Undirected Networks, International Journal of Modern Physics C, Vol. 19 (5), 2008, 717-726
• 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, Vol. 194 (1), 2007, 7-20
• Dehmer M., Mehler A.: A new Method of Measuring Similarity for a Special Class of Directed Graphs, Tatra Mountains Mathematical Publications, 2007, Vol. 36, 39-59
• Dehmer M., Emmert-Streib F.: Comparing Large Graphs Efficiently by Margins of Feature Vec- tors, Applied Mathematics and Computation, Vol. 188 (2), 2007, 1699-1710
• Emmert-Streib F., Dehmer M.: Nonlinear Time Series Prediction based on a Power-Law Noise Model, International Journal of Modern Physics C, Vol. 18 (12), 1839-1852, 2007
• Emmert-Streib F., Dehmer M.: Information Theoretic Measures of UHG Graphs with Low Com- putational 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 Computation, Vol. 186 (2), 2007, 1326-1333
• 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, Ro- stocker Informatik Berichte, Vol. 30 (2), 2006, 47-57
• Dehmer M.: On the Location of Zeros for complex Polynomials, Journal of Inequalities in Pure and Applied Mathematics, Vol. 7 (1), 2006
• 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
• DehmerM.:DataMining-KonzepteundgraphentheoretischeMethodenzurAnalyseweb-basierter Daten, Journal of Computational Linguistics and Language Technology, 2005, 113-143
• Emmert-StreibF.,DehmerM.,LiuJ.,MühlhäuserM.:RankingGenesfromDNAMicroarrayData of Cervical Cancer by a local Tree Comparison, International Journal of Biomedical Science, Vol. 1 (1), 2005, 17-2217

Peer-Reviewed Conference Publications

• Tripathi S., Strasser S., Mittermayr C., Dehmer M., Jodlbauer H., Approaches to Identify Rele- vant Process Variables in Injection Moulding using Beta Regression and SVM, In: Proceedings of the 8-th International Conference on Data Science, Technology and Applications, Prague, Czech Republic, 2019, 233-242
• Dehmer M., Pickl S., Wang Z.: A Survey on Statistical Network Analysis, In: Proceedings of the 2015 Conference on Foundations in Computer Science, Las Vegas, USA, Vol. 2, 2015
• Dehmer M., Lechleuthner A., Mudimu O. A., Pickl S.: Exploring Data Analysis Techniques for Threat Estimation, In: Proceedings of Future Security, Berlin, Germany, 2015
• Dehmer M., Nistor M. S., Schmitz W., Neubecker K. A.: Aspects of Quantitative Analysis of Transportation Networks, In: Proceedings of Future Security, Berlin, Germany, 2015
• Nistor M. S., Bein D., Bein W., Dehmer M., Pickl S.: Time-based estimation of vulnerable points in the munich subway network, In: Operations Research Proceedings, Springer, 2015, 355-360
• Dehmer M., Holzinger A., Emmert-Streib F.: Personalized Medicine by Means of Complex Net- works – A Big Data Challenge, In: Proceedings of Big Data, Editors: Weber R. H., Thouvenin F., Zürich 2014, Switzerland
• Müller L. A. J., Kugler K. G., Dehmer M.: Structural Analysis of Molecular Networks: AMES Mu- tagenicity, In: Proceedings of the 2011 Conference on Bioinformatics & Computational Biology (BIOCOMP’11), Las Vegas, USA, Vol. 1, 2011, 196-201
• Kugler K. G., Müller L. A. J., Dehmer M.: Analysis of Metabolic Networks: On the Similarity of the Three Domains of Life, In: Proceedings of the 2011 Conference on Bioinformatics & Computational Biology (BIOCOMP’11), Las Vegas, USA, Vol. 2, 2011, 361-366
• Kugler G. G., Müller L. A. J., Gallasch R. K., Graber A., Dehmer M.: A Novel Majority Vote Count Algorithm for Integrative Analysis of Association Networks, In: Proceedings of the 2010 Conference on Bioinformatics & Computational Biology (BIOCOMP’10), Las Vegas, USA, 2010, 62-67
• Müller L. A. J., Kugler G. G., Dander A., Graber A., Dehmer M.: A Network-based Approach to Classify Disease Stages of Prostate Cancer Using Quantitative Network Measures, In: Pro- ceedings of the 2010 Conference on Bioinformatics & Computational Biology (BIOCOMP’10), Las Vegas, USA, 2010, 55-61
• Borgert S., Dehmer M., Aitenbichler E.: A Comparative Study of Complexity Measure to Ana- lyze Business Process Models, In: Proceedings of UICS’2009, International Symposium Under- standing Intelligent and Complex Systems, 2009
• Dehmer M., Borgert S.: On the Information Content of Weighted Chemical Structures, In: Pro- ceedings of UICS’2009, International Symposium Understanding Intelligent and Complex Sys- tems, 2009
• Dehmer M., Emmert-Streib F.: Towards Network Complexity. In: Proceedings of COMPLEX’2009 – The First International Conference on Complex Sciences: Theory and Applications, Springer, Lecture Notes, Vol. 4, 2009, 707-714
• Emmert-Streib F., Dehmer M.: Towards a Partitioning of the Input Space of Boolean Networks: Variable Selection using Bagging. In: Proceedings of COMPLEX’2009 – The First International Conference on Complex Sciences: Theory and Applications, Springer, Lecture Notes, Vol. 4, 2009, 715-723
• Emmert-Streib F., Dehmer M.: Organizational Structure of the Transcriptional Regulatory Net- work of Yeast: Periodic Genes. In: Proceedings of COMPLEX’2009 – The First International Conference on Complex Sciences: Theory and Applications, Springer, Lecture Notes, Vol. 4 2009, 140-14818
• Dehmer M., Borgert S., Emmert-Streib F.: Network Classes and Graph Complexity Measures. In: Proceedings of CANS’2008 – Workshop on Complexity and Intelligence of Artificial and Nat- ural Systems, IEEE Computer Society Press, Targu Mures, Romania, 2008
• Dehmer M., Borgert S., Emmert-Streib F.: Investigating Network Classes by Measuring Their Complexity. In: Proceedings of CANS’2008 – Workshop on Complexity and Intelligence of Artifi- cial and Natural Systems, Petru Maior University Press, Targu Mures, Romania, 2008
• Dehmer M.: Relations Between the Topological Complexities of Complex Networks, In: Pro- ceedings of the 2008 International Conference on Machine Learning: Models, Technologies & Applications (MLMTA’08), Las Vegas, USA, 2008
• Emmert-Streib F., Dehmer M.: Towards a channel capacity of communication networks. In: Proceedings of the first International Conference on Complexity and Intelligence of the Artifi- cial and Natural Complex Systems, Medical Applications of the Complex Systems, Biomedical Computing, IEEE Computer Society Press, Targu Mures, Romania, 2008
• Emmert-Streib F., Dehmer M.: Quantifying Communication Capabilities of Networks. In: Pro- ceedings of the first International Conference on Complexity and Intelligence of the Artificial and Natural Complex Systems, Medical Applications of the Complex Systems, Biomedical Comput- ing, IEEE Computer Society Press, Targu Mures, Romania, 2008
• Dehmer M., Mehler A., Emmert-Streib F.: Graph-theoretical Characterizations of Generalized Trees, Proceedings of the 2007 International Conference on Machine Learning: Models, Tech- nologies & Applications (MLMTA’07), Las Vegas, USA, 2007
• Dehmer M., Emmert-Streib F., Zulauf A.: A Graph Mining Technique for Automatic Classification of Web Genre Data, In: Proceedings of the 2007 International Conference on Machine Learning: Models, Technologies & Applications (MLMTA’07), Las Vegas, USA, 2007
• Emmert-Streib F., Dehmer M.: Global information processing in gene networks: Fault Tolerance, In: Proceedings of the bio-inspired models of network, information, and computing systems, Bionetics 2007, 326-329
• 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, Vol. 4827, 2007, 540-549
• 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), 2007, 142-149
• 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 & Com- putational Biology, Gene Networks: Theory and Application, Workshop at BIOCOMP’06, Las Vegas, USA, 2006, 472-476
• 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, Workshop at BIOCOMP’06, Las Vegas, USA, 2006, 477-482
• 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, 67-74
• 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, 304-31019
• Dehmer M., Emmert-Streib F., Mehler A., Kilian J., Mühlhäuser M.: Application of a Similarity Measure for Graphs to web-based Documents, VI. International Conference on Enformatika, Systems Sciences and Engineering, Budapest, Hungary, Enformatika 8, 2005, 77-81
• 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 Sci- ences 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 En- formatika, Systems Sciences and Engineering, Krakow, Poland, Enformatika 10, 2005, 70-74
• Emmert-Streib F., Dehmer M., Liu J., Mühlhäuser M.: A Systems Approach to Gene Ranking for DNA Microarray Data for Cervical Cancer, VI. International Conference on Enformatika, Sci- ences and Engineering, Budapest, Hungary, Enformatika 8, 2005, 82-87
• Emmert-Streib F., Dehmer M.: A Systems Biology approach for the classification of DNA Mi- croarray Data, Applications of Statistical and Machine Learning. Methods in Bioinformatics, Proceedings of BIT 2005 – Bioinformatics Workshop,Torun, Poland, September 15-16, 2005
• Emmert-Streib F., Dehmer M., Kilian J.: Classification of large Graphs by a local Graph De- composition, Proceedings of the 2005 International Conference on Data Mining (DMIN’05), Las Vegas, USA, Editors: Arabnia H. R., Scime A., 2005, 200-207
• Mehler A., Gleim R., Dehmer M.: Towards Structure-Sensitive Hypertext Categorization. In: Spiliopoulou M., Kruse R., Borgelt C., Nürnberger A., Gaul W. (Editors): Proceedings of the 29th Annual Conference of the German Classification Society, Universität Magdeburg, 2005, Berlin, New York: Springer, 406-413
• Mehler A., Dehmer M., Gleim R.: Zur automatischen Klassifikation von Webgenres, In: Fisseni B., Schmitz H. C., Schröder B., Wagner P. (Editors): Sprachtechnologie, mobile Kommunikation und linguistische Ressourcen. Beiträge zur GLDV-Tagung 2005, Universität Bonn, Peter Lang Publishing, 158-174
• Mehler A., Dehmer M., Gleim R.: Towards logical Hypertext Structure. A graph-theoretic Per- spective, Proceedings of I2CS’04, Lecture Notes, Berlin, New York: Springer, 2005, 136-150
• Dehmer M., Mehler A., Gleim R.: Aspekte der Kategorisierung von Webseiten, Lecture Notes in Computer Science, Springer, Jahrestagung der Gesellschaft für Informatik, Ulm, Germany, 2004, 39-43