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A First Course in Network Science

A First Course in Network Science

£34.99

textbook
  • Publication planned for: March 2020
  • availability: Not yet published - available from March 2020
  • format: Hardback
  • isbn: 9781108471138

£ 34.99
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About the Authors
  • 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.

    • Specifically designed for introductory-level undergraduate courses in disciplines that use networks, such as business, communications, cognitive science, neuroscience, sociology, biology, and engineering, among others
    • Utilizes clear language with no technical jargon and extensive use of full color figures to illustrate key concepts
    • Allows students to immediately start writing Python code to manipulate, visualize, and analyze real-world networks
    • Includes an abundance of worked examples and homework problems to reinforce students' understanding of the material and an extensive suite of programming tutorials are hosted online to develop practical coding skills
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    Product details

    • Publication planned for: March 2020
    • format: Hardback
    • isbn: 9781108471138
    • dimensions: 246 x 189 mm
    • availability: Not yet published - available from March 2020
  • Table of Contents

    Preface
    Introduction
    1. Network elements
    2. Small worlds
    3. Hubs
    4. Directions and weights
    5. Network models
    6. Communities
    7. Dynamics
    Appendix A. Python tutorial
    Appendix B. NetLogo models
    Bibliography
    Index.

  • Authors

    Filippo Menczer, Indiana University, Bloomington
    Filippo Menczer is Professor of Informatics and Computing at Indiana University, Bloomington. He is an ACM Distinguished Scientist and board member of the Indiana University Network Science Institute. He serves in editorial roles for several leading journals including Network Science, EPJ Data Science, and PeerJ: Computer Science. His research focuses on network science, computational social science, and Web science, with a focus on countering social media manipulation. His work on the spread of misinformation has received worldwide news coverage.

    Santo Fortunato, Indiana University, Bloomington
    Santo Fortunato is Director of the Network Science Institute and Professor of Informatics at Indiana University, Bloomington. His current research is focused on network science, specifically network community detection, computational social science, and the 'science of science'. He received the German Physical Society's Young Scientist Award for Sociophysics and Econophysics in 2011 for his important contributions to the physics of social systems. He is Founding Chair of the International Conference on Computational Social Science (IC2S2).

    Clayton A. Davis, Indiana University, Bloomington
    Clayton A. Davis is an Informatics Ph.D. candidate at Indiana University, Bloomington and he holds B.S. and M.A. degrees in Mathematics. His research is concerned with the development of big-data platforms for social media analytics, machine learning algorithms for combating online abuse, design of crowdsourcing platforms, and the role of social media in social movements. His work on social bot detection was featured in major news outlets worldwide. His Web tools, including Botometer, Kinsey Reporter, and the Observatory on Social Media, answer millions of queries from thousands of users weekly. He won the 2017 Informatics Associate Instructor Award for his role in the development of high-quality teaching material for network science courses.

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