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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. Suitable for both graduate students and researchers across a range of disciplines, this novel text provides a fast-track to network data expertise.
This chapter uses the ideas of hydrodynamics introduced in the last chapter to formulate the hydrodynamic theory of the flocking problem (i.e., the “Toner–Tu” equations).
This chapter “derives” the scaling laws found in the previous chapter for incompressible flocks, using a simple heuristic argument which gives some physical insight into the mechanism, and its essentially nonequilibrium nature.
I present a purely dynamical derivation of the Mermin–Wagner–Hohenberg theorem, and compare it with the standard equilibrium derivation. This also provides an opportunity to introduce diffusion equations and gradient expansions, both of which play a large role in what follows.
I introduce, and describe in detail, the dynamical renormalization group, using the KPZ equation as an example. In addition to spelling out the mechanics of the technique in great detail, I also emphasize its philosophical importance, as the answer to Einstein’s famous question “Why is the universe intelligible?” and its role as a guide to the formulation of hydrodynamic theories.