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.
| About the Editors |
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vii | |
| Contributors |
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ix | |
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1 The Spread of Strategies Predicted by Artificial Intelligence in Networks |
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1 | (28) |
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2 The Spread of Multiple Strategies in the Complex Networks |
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29 | (40) |
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3 The Epidemic Spreading Processes in Complex Networks |
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69 | (30) |
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4 Measurements for Investigating Complex Networks |
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99 | (30) |
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5 Overview of Social Media Content and Network Analysis |
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129 | (14) |
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6 Analysis of Critical Infrastructure Network |
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143 | (30) |
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7 Evolving Networks and Their Vulnerabilities |
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173 | (20) |
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8 Review of Structures and Dynamics of Economic Complex Networks: Large Scale Payment Network of Estonia |
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193 | (34) |
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Stephanie Rendon de la Torre |
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9 Predicting Macroeconomic Variables Using Financial Networks Properties |
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227 | (12) |
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10 Anomaly Detection in Dynamic Complex Networks |
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239 | (26) |
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11 Finding Justice through Network Analysis |
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265 | (16) |
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| Index |
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281 | |
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.
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