A First Course in Network Science

Höfundar: Filippo Menczer; Santo Fortunato; Clayton A. Davis (Útgáfa: 0)
A First Course in Network Science

Kaup valmöguleikar

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.

Nánar um bókina

Útgefandi
Cambridge University Press
ISBN
9781108579612
Print ISBN
9781108471138
Format
ePub
Útgáfa
0
Höfundar
Filippo Menczer; Santo Fortunato; Clayton A. Davis
Tungumál
English
Útgefið
2020-02-06
Prent takmörkun á líftíma
10
Prent takmörkun
2
Afritunar takmörkun
2

Kaflar

  • Cover
  • Half-title page
  • Title page
  • Imprints page
  • Dedication
  • Contents
  • 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