Mathematical Foundations and Applications of Graph Entropy

Introduction to Graph Entropy
Entropy of Horizontal Visibility Graphs
Measuring network's entropy in ADHD: A new approach to investigate neuropsychiatric disorders
Statistical methods in graphs: estimation, model selection, and test
Graph entropies and text texture measures
Graph Complexity: An Information Theoretical Approach
Prediction of Molecular Properties and Activities using Information-Theoretical Topological Indices
Generalized entropies of complex and random networks
Identifying node importance based on information entropy in complex networks
Time Latency in Networked Operations: Effect of Human in The Loop
Information flow and entropy production on Bayesian networks
Applications of graph entropy
Entropy, Counting, and Graphs

Matthias Dehmer studied mathematics at the University of Siegen (Germany) and received his Ph.D. in computer science from the Technical University of Darmstadt (Germany). Afterwards, he was a research fellow at Vienna Bio Center (Austria), Vienna University of Technology, and University of Coimbra (Portugal). He obtained his habilitation in applied discrete mathematics from the Vienna University of Technology. Currently, he is Professor at UMIT - The Health and Life Sciences University (Austria) and also holds a position at the Universität der Bundeswehr München. His research interests are in applied mathematics, bioinformatics, systems biology, graph theory, complexity and information theory. He has written over 180 publications in his research areas.
 
Frank Emmert-Streib studied physics at the University of Siegen (Germany) gaining his PhD in theoretical physics from the University of Bremen (Germany). He received postdoctoral training from the Stowers Institute for Medical Research (Kansas City, USA) and the University of Washington (Seattle, USA). Currently, he is associate professor for Computational Biology at Tampere University of Technology (Finland). His main research interests are in the field of computational medicine, network biology and statistical genomics.