Big Data of Complex Networks presents and explains the methods from the study of big data that can be used in analysing massive structural data sets, including both very large networks and sets of graphs. As well as applying statistical analysis techniques like sampling and bootstrapping in an interdisciplinary manner to produce novel techniques for analyzing massive amounts of data, this book also explores the possibilities offered by the special aspects such as computer memory in investigating large sets of complex networks.

Intended for computer scientists, statisticians and mathematicians interested in the big data and networks, Big Data of Complex Networks is also a valuable tool for researchers in the fields of visualization, data analysis, computer vision and bioinformatics.

Key features:

Matthias Dehmer received his PhD in computer science from the Darmstadt University of Technology, Germany. Currently, he is Professor at UMIT – The Health and Life Sciences University, Austria, and the Universität der Bundeswehr München. His research interests are in graph theory, data science, complex networks, complexity, statistics and information theory.

Frank Emmert-Streib received his PhD in theoretical physics from the University of Bremen, and is currently Associate professor at Tampere University of Technology, Finland. His research interests are in the field of computational biology, machine learning and network medicine.

Stefan Pickl holds a PhD in mathematics from the Darmstadt University of Technology, and is currently a Professor at Bundeswehr Universität München. His research interests are in operations research, systems biology, graph theory and discrete optimization.

Andreas Holzinger received his PhD in cognitive science from Graz University and his habilitation (second PhD) in computer science from Graz University of Technology. He is head of the Holzinger Group HCI-KDD at the Medical University Graz and Visiting Professor for Machine Learning in Health Informatics Vienna University of Technology.

Big Data of Complex Networks presents and explains the methods from the study of big data that can be used in analysing massive structural data sets, including both very large networks and sets of graphs. As well as applying statistical analysis techniques like sampling and bootstrapping in an interdisciplinary manner to produce novel techniques for analyzing massive amounts of data, this book also explores the possibilities offered by the special aspects such as computer memory in investigating large sets of complex networks.

Intended for computer scientists, statisticians and mathematicians interested in the big data and networks, Big Data of Complex Networks is also a valuable tool for researchers in the fields of visualization, data analysis, computer vision and bioinformatics.

Key features:

Matthias Dehmer received his PhD in computer science from the Darmstadt University of Technology, Germany. Currently, he is Professor at UMIT – The Health and Life Sciences University, Austria, and the Universität der Bundeswehr München. His research interests are in graph theory, data science, complex networks, complexity, statistics and information theory.

Frank Emmert-Streib received his PhD in theoretical physics from the University of Bremen, and is currently Associate professor at Tampere University of Technology, Finland. His research interests are in the field of computational biology, machine learning and network medicine.

Stefan Pickl holds a PhD in mathematics from the Darmstadt University of Technology, and is currently a Professor at Bundeswehr Universität München. His research interests are in operations research, systems biology, graph theory and discrete optimization.

Andreas Holzinger received his PhD in cognitive science from Graz University and his habilitation (second PhD) in computer science from Graz University of Technology. He is head of the Holzinger Group HCI-KDD at the Medical University Graz and Visiting Professor for Machine Learning in Health Informatics Vienna University of Technology.

This book focuses on novel design and systems engineering approaches, including theories and best practices, for promoting a better integration of people and engineering systems. It covers a range of hot topics related to: development of human-centered systems; interface design and human-computer interaction; usability and user experience; emergent properties of human behavior; innovative materials in manufacturing, biomechanics, and sports medicine, safety engineering and systems complexity business analytics, design and technology and many more.  The book, which gathers selected papers presented at the 2nd International Conference on Human Systems Engineering and Design: Future Trends and Applications (IHSED 2019), held on September 16-18, 2019, at Universität der Bundeswehr München, Munich, Germany, provides researchers, practitioners and program managers with a snapshot of the state-of-the-art and current challenges in the field of human systems engineering and design.

The 30 full papers together with 2 short papers included in the volume were carefully reviewed and selected from 50 submissions. They are organized in the following topical sections: M&S of intelligent systems,  R&D and application; and AxS/AI in context of future warfare and security environment and future challenges of Advance M&S Technology.

The 21 full papers included in the volume were carefully reviewed and selected from 24 submissions. They are organized in the following topical sections: Modelling, Simulation Technology, methodologies and Robotics.

This book contains the proceedings of the 19th Cologne-Twente Workshop on Graphs and Combinatorial Optimization, held during June 20-22, 2023, in Garmisch-Partenkirchen, Germany. This successful series of international workshops is known to attract high-quality research on the theory and application of discrete algorithms, graphs, and combinatorial optimization in a wide sense. The papers collected in this book represent cutting-edge research by leading researchers and attract a broad readership in academia worldwide. The book is addressed to researchers and advanced students, but also to professionals in industry concerned with algorithm design and optimization problems in different areas of application.

This book constitutes the refereed proceedings of the 18th International Conference on Critical Information Infrastructures Security, CRITIS 2023, held in Helsinki region, Finland, during September 13–15 2023.

The 13 full papers and 3 short papers and one Paper in Progress were carefully selected from 25 submissions. The conference addressed four scientific focus areas, namely information, infrastructures, security and hybrid threats in an interdisciplinary manner and specifically in the context-based analyses and complex optimization methods.

This volume LNCS 14615 constitutes the refereed proceedings of the 10th International Conference on Modelling and Simulation for Autonomous Systems, MESAS 2023, in October 17–19, 2023, in Palermo, Italy.

The 21 full papers presented together with 4 short papers were carefully reviewed and selected from 49 submissions. The conference focuses to to unite the Modelling and Simulation and the Autonomous Systems/Robotic communities, creating a space for the exchange of innovative ideas and concept development.  

Big Data of Complex Networks presents and explains the methods from the study of big data that can be used in analysing massive structural data sets, including both very large networks and sets of graphs. As well as applying statistical analysis techniques like sampling and bootstrapping in an interdisciplinary manner to produce novel techniques for analyzing massive amounts of data, this book also explores the possibilities offered by the special aspects such as computer memory in investigating large sets of complex networks.

Intended for computer scientists, statisticians and mathematicians interested in the big data and networks, Big Data of Complex Networks is also a valuable tool for researchers in the fields of visualization, data analysis, computer vision and bioinformatics.

Key features:

Matthias Dehmer received his PhD in computer science from the Darmstadt University of Technology, Germany. Currently, he is Professor at UMIT – The Health and Life Sciences University, Austria, and the Universität der Bundeswehr München. His research interests are in graph theory, data science, complex networks, complexity, statistics and information theory.

Frank Emmert-Streib received his PhD in theoretical physics from the University of Bremen, and is currently Associate professor at Tampere University of Technology, Finland. His research interests are in the field of computational biology, machine learning and network medicine.

Stefan Pickl holds a PhD in mathematics from the Darmstadt University of Technology, and is currently a Professor at Bundeswehr Universität München. His research interests are in operations research, systems biology, graph theory and discrete optimization.

Andreas Holzinger received his PhD in cognitive science from Graz University and his habilitation (second PhD) in computer science from Graz University of Technology. He is head of the Holzinger Group HCI-KDD at the Medical University Graz and Visiting Professor for Machine Learning in Health Informatics Vienna University of Technology.

Big Data of Complex Networks presents and explains the methods from the study of big data that can be used in analysing massive structural data sets, including both very large networks and sets of graphs. As well as applying statistical analysis techniques like sampling and bootstrapping in an interdisciplinary manner to produce novel techniques for analyzing massive amounts of data, this book also explores the possibilities offered by the special aspects such as computer memory in investigating large sets of complex networks.

Intended for computer scientists, statisticians and mathematicians interested in the big data and networks, Big Data of Complex Networks is also a valuable tool for researchers in the fields of visualization, data analysis, computer vision and bioinformatics.

Key features:

Matthias Dehmer received his PhD in computer science from the Darmstadt University of Technology, Germany. Currently, he is Professor at UMIT – The Health and Life Sciences University, Austria, and the Universität der Bundeswehr München. His research interests are in graph theory, data science, complex networks, complexity, statistics and information theory.

Frank Emmert-Streib received his PhD in theoretical physics from the University of Bremen, and is currently Associate professor at Tampere University of Technology, Finland. His research interests are in the field of computational biology, machine learning and network medicine.

Stefan Pickl holds a PhD in mathematics from the Darmstadt University of Technology, and is currently a Professor at Bundeswehr Universität München. His research interests are in operations research, systems biology, graph theory and discrete optimization.

Andreas Holzinger received his PhD in cognitive science from Graz University and his habilitation (second PhD) in computer science from Graz University of Technology. He is head of the Holzinger Group HCI-KDD at the Medical University Graz and Visiting Professor for Machine Learning in Health Informatics Vienna University of Technology.

This book focuses on novel design and systems engineering approaches, including theories and best practices, for promoting a better integration of people and engineering systems. It covers a range of hot topics related to: development of human-centered systems; interface design and human-computer interaction; usability and user experience; emergent properties of human behavior; innovative materials in manufacturing, biomechanics, and sports medicine, safety engineering and systems complexity business analytics, design and technology and many more.  The book, which gathers selected papers presented at the 2nd International Conference on Human Systems Engineering and Design: Future Trends and Applications (IHSED 2019), held on September 16-18, 2019, at Universität der Bundeswehr München, Munich, Germany, provides researchers, practitioners and program managers with a snapshot of the state-of-the-art and current challenges in the field of human systems engineering and design.

The 30 full papers together with 2 short papers included in the volume were carefully reviewed and selected from 50 submissions. They are organized in the following topical sections: M&S of intelligent systems,  R&D and application; and AxS/AI in context of future warfare and security environment and future challenges of Advance M&S Technology.

The 21 full papers included in the volume were carefully reviewed and selected from 24 submissions. They are organized in the following topical sections: Modelling, Simulation Technology, methodologies and Robotics.

This book contains the proceedings of the 19th Cologne-Twente Workshop on Graphs and Combinatorial Optimization, held during June 20-22, 2023, in Garmisch-Partenkirchen, Germany. This successful series of international workshops is known to attract high-quality research on the theory and application of discrete algorithms, graphs, and combinatorial optimization in a wide sense. The papers collected in this book represent cutting-edge research by leading researchers and attract a broad readership in academia worldwide. The book is addressed to researchers and advanced students, but also to professionals in industry concerned with algorithm design and optimization problems in different areas of application.

This book constitutes the refereed proceedings of the 18th International Conference on Critical Information Infrastructures Security, CRITIS 2023, held in Helsinki region, Finland, during September 13–15 2023.

The 13 full papers and 3 short papers and one Paper in Progress were carefully selected from 25 submissions. The conference addressed four scientific focus areas, namely information, infrastructures, security and hybrid threats in an interdisciplinary manner and specifically in the context-based analyses and complex optimization methods.

This volume LNCS 14615 constitutes the refereed proceedings of the 10th International Conference on Modelling and Simulation for Autonomous Systems, MESAS 2023, in October 17–19, 2023, in Palermo, Italy.

The 21 full papers presented together with 4 short papers were carefully reviewed and selected from 49 submissions. The conference focuses to to unite the Modelling and Simulation and the Autonomous Systems/Robotic communities, creating a space for the exchange of innovative ideas and concept development.