Modern and Interdisciplinary Problems in Network Science A Translational Research Perspective

Modern and Interdisciplinary Problems in Network Science: A Translational Research Perspective covers a broad range of concepts and methods, with a strong emphasis on interdisciplinarity. The topics range from analyzing mathematical properties of network-based methods to applying them to application areas. By covering this broad range of topics, the book aims to fill a gap in the contemporary literature in disciplines such as physics, applied mathematics and information sciences.

Table of Contents Chapter 1: The Spread of Strategies Predicted by Artificial Intelligence in Networks Chunyan Zhang, Nankai University, China Chapter 2: The Spread of Multiple Strategies in the Complex Networks Jenlei Zhang, Nankai University, China Chapter 3: The epidemic spreading processes in complex networks, Chengyi Xia, Tianjin University of Technology, China Chapter 4: Measurements for Investigating complex networks Z. Chen; Y. Shi; M. Dehmer; Nankai University, China, Austria Chapter 5: Overview of Social Media Content and Network Analysis Mohammed Ali Al-Garadi, University of Malaya, Kuala Lumpur, Malaysia Chapter 6: Analysis of Critical Infrastructure Network D. Rehak, P. Senovsky, M. Hromada; VSB – Technical University of Ostrava, Czech Republic Chapter 7: Evolving Networks and their Vulnerabilities, Abbe Mowshowitz, Valia Mitsou, Graham Bent Chapter 8: Review of structures and dynamics of economic complex networks: Large scale payment network of Estonia Stephanie Rendón de la Torre, Tallinn University of Technology, Estonia Chapter 9: Predicting Macroeconomic Variables using Financial Networks Properties Petre Caraiani, Institute for Economic Forecasting, Romanian Academy, Romania Chapter 10: Anomaly Detection in Complex Networks Yaser Yasami, Department of Computer Engineering, Payame Noor University (PNU), Tehran, Iran Chapter 11: Finding Justice through Network Analysis Radboud Winkels, PPLE College Amsterdam, Holland

Zengqiang Chen works at the Department of Automation at Nankai University, where he is currently a professor. His research interests are in complex networks, multi-agent systems, computer application systems, nonlinear dynamic control, intelligent computing and stochastic analysis. Matthias Dehmer is a professor at University of Applied Sciences Upper Austria and UMIT - The Health and Life Sciences University. He also holds a guest professorship at Nankai University. His research interests are in graph theory, complex networks, complexity, machine big data, analytics, and information theory. In particular, he is also working on machine learning-based methods to design new data analysis methods for solving problems in manufacturing and production. Frank Emmert-Streib is a professor at Tampere University Technology, Finland, in the Department of Signal Processing. His research interests are in the field of computational biology, data science and analytics in the development and application of methods from statistics and machine learning for the analysis of big data from genomics, finance and business. Yongtang Shi is a professor at the Center for Combinatorics of Nankai University. His research interests are in graph theory and its applications, especially the applications of graph theory in mathematical chemistry, computer science and information theory.