A First Course in Network Science

"Networks are everywhere: networks of friends, transportation networks and the Web. Neurons in our brains and proteins within our bodies form networks that determine our intelligence and survival. This modern, accessible textbook introduces the basics of network science for a wide range of job sectors from management to marketing, from biology to engineering, and from neuroscience to the social sciences. Students will develop important, practical skills and learn to write code for using networks in their areas of interest - even as they are just learning to program with Python. Extensive sets of tutorials and homework problems provide plenty of hands-on practice and longer programming tutorials online further enhance students' programming skills. This intuitive and direct approach makes the book ideal for a first course, aimed at a wide audience without a strong background in mathematics or computing but with a desire to learn the fundamentals and applications of network science."--Publisher's website Preface Acknowledgments 0 Introduction 0.1 Social Networks 0.2 Communication Networks 0.3 The Web and Wikipedia 0.4 The Internet 0.5 Transportation Networks 0.6 Biological Networks 0.7 Summary 0.8 Further Reading Exercises 1 Network Elements 1.1 Basic Definitions 1.2 Handling Networks in Code 1.3 Density and Sparsity 1.4 Subnetworks 1.5 Degree 1.6 Directed Networks 1.7 Weighted Networks 1.8 Multilayer and Temporal Networks 1.9 Network Representations 1.10 Drawing Networks 1.11 Summary 1.12 Further Reading Exercises 2 Small Worlds 2.1 Birds of a Feather 2.2 Paths and Distances 2.3 Connectedness and Components 2.4 Trees 2.5 Finding Shortest Paths 2.6 Social Distance 2.7 Six Degrees of Separation 2.8 Friend of a Friend 2.9 Summary 2.10 Further Reading Exercises 3 Hubs 3.1 Centrality Measures 3.2 Centrality Distributions 3.3 The Friendship Paradox 3.4 Ultra-Small Worlds 3.5 Robustness 3.6 Core Decomposition 3.7 Summary 3.8 Further Reading Exercises 4 Directions and Weights 4.1 Directed Networks 4.2 The Web 4.3 PageRank 4.4 Weighted Networks 4.5 Information and Misinformation 4.6 Co-occurrence Networks 4.7 Weight Heterogeneity 4.8 Summary 4.9 Further Reading Exercises 5 Network Models 5.1 Random Networks 5.2 Small Worlds 5.3 Configuration Model 5.4 Preferential Attachment 5.5 Other Preferential Models 5.6 Summary 5.7 Further Reading Exercises 6 Communities 6.1 Basic Definitions 6.2 Related Problems 6.3 Community Detection 6.4 Method Evaluation 6.5 Summary 6.6 Further Reading Exercises 7 Dynamics 7.1 Ideas, Information, Influence 7.2 Epidemic Spreading 7.3 Opinion Dynamics 7.4 Search 7.5 Summary 7.6 Further Reading Exercises Appendix A Python Tutorial A.1 Jupyter Notebook A.2 Conditionals A.3 Lists A.4 Loops A.5 Tuples A.6 Dictionaries A.7 Combining Data Types Appendix B NetLogo Models B.1 PageRank B.2 Giant Component B.3 Small Worlds B.4 Preferential Attachment B.5 Virus on a Network B.6 Language Change References Index A practical introduction to network science suitable for students studying diverse programs such as business, cognitive science, neuroscience, sociology, biology, and engineering. A wide range of examples and exercises develop readers' understanding, and Python programming tutorials provided online reinforce coding skills. A Practical Introduction To Network Science For Students Across Business, Cognitive Science, Neuroscience, Sociology, Biology, Engineering And Other Disciplines.