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Overview: Drawing examples from real-world networks, this essential book traces the methods behind network analysis and explains how network data is first gathered, then processed and interpreted. The text will equip you with a toolbox of diverse methods and data modelling approaches, allowing you to quickly start making your own calculations on a huge variety of networked systems. This book sets you up to succeed, addressing the questions of what you need to know and what to do with it, when beginning to work with network data. The hands-on approach adopted throughout means that beginners quickly become capable practitioners, guided by a wealth of interesting examples that demonstrate key concepts. Exercises using real-world data extend and deepen your understanding, and develop effective working patterns in network calculations and analysis. There are great textbooks on network science. We complement these with a focus on the practical side of network science—working with network data. The purpose of this book is to provide a more practical guide for data scientists to use network science. Machine Learning, also called statistical learning, is the science and technology of building predictive models that generalize from data. Machine Learning has not only disrupted many industries, it’s revolutionizing how science is done. Because of its ubiquity, network data are also heavily leveraged by Machine Learning. Thus, scientists working with network data (and all forms of data) can benefit from the tools and techniques of Machine Learning. There are several types of Machine Learning methods: supervised learning and unsupervised learning are the primary categories and then there are self-supervised learning, reinforcement learning, and more. Suitable for both graduate students and researchers across a range of disciplines, this novel text provides a fast-track to network data expertise.
Genre: Non-Fiction > Tech & Devices
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