📖 Read pages 13-79 of 288 of Linked: The New Science Of Networks by Albert-László Barabási

📖 Read pages 13-79 of 288 of Linked: The New Science Of Networks by Albert-László Barabási

It’s an interesting overview of the subject of network science and complexity. Potentially good if you know nothing of the area at all, or if you’re about to delve heavily into the topic. I’m breezing through it quickly with an eye toward reading his more technical level networks textbook that came out two years ago as well as some of his papers in the area.

Some of the pieces so far are relatively overwritten given that it’s now more than 15 years later… but the general audience then probably needed the extra back story. The only math so far is at the level of simple logarithms and the few equations are buried in the footnotes.

There are some useful rules of thumb he’s introduced for the generalists and engineers in the crowd like the idea of things that fall into an 80/20 Pareto rule are very likely power law models.

He’s repeated some common stories about Paul ErdÅ‘s and Alfréd Rényi. I hadn’t heard the story about ErdÅ‘s saying there were too many plus signs on the Notre Dame campus–that was kind of cute. I did enjoy that he’d dug at least an additional layer deeper to pull up Frigyes Karinthy’s short story “Chains” to introduce the original(?) conceptualization of the idea of Six Degrees of Separation.

I’ll circle back later for additional highlights and annotations.

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Chris Aldrich

I'm a biomedical and electrical engineer with interests in information theory, complexity, evolution, genetics, signal processing, IndieWeb, theoretical mathematics, and big history. I'm also a talent manager-producer-publisher in the entertainment industry with expertise in representation, distribution, finance, production, content delivery, and new media.

5 thoughts on “📖 Read pages 13-79 of 288 of Linked: The New Science Of Networks by Albert-László Barabási”

  1. Bookmarked Collective dynamics of ‘small-world’ networks by Duncan J. Watts & Steven H. Strogatz (Nature | VOL 393)

    Networks of coupled dynamical systems have been used to model biological oscillators1–4, Josephson junction arrays5,6, excitable media7, neural networks8–10, spatial games11, genetic control networks12 and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them ‘small-world’ networks, by analogy with the small-world phenomenon13,14 (popularly known as six degrees of separation15). The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display
    enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.

    hat tip: Linked: The New Science Of Networks by Albert-László Barabási
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  2. Bookmarked On Random Graphs. I by Paul Erdős and Alfréd Rényi (Publicationes Mathematicae. 6: 290–297.)

    Original source of Erdős–Rényi model.

    In the mathematical field of graph theory, the Erdős–Rényi model is either of two closely related models for generating random graphs. They are named after mathematicians Paul Erdős and Alfréd Rényi, who first introduced one of the models in 1959,[1][2] while Edgar Gilbert introduced the other model contemporaneously and independently of Erdős and Rényi.[3] In the model of Erdős and Rényi, all graphs on a fixed vertex set with a fixed number of edges are equally likely; in the model introduced by Gilbert, each edge has a fixed probability of being present or absent, independently of the other edges. These models can be used in the probabilistic method to prove the existence of graphs satisfying various properties, or to provide a rigorous definition of what it means for a property to hold for almost all graphs.

    hat tip: Linked: The New Science Of Networks by Albert-László Barabási
    Syndicated copies to:

    Syndicated copies:

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  5. @grayareas I’ve been reading journal articles about complexity theory off and on for a few years and know that he’s written a network theory textbook a year or so ago. I thought I’d breeze through this as a quick introduction before delving into the theory more deeply. It’s a pretty solid overview so far.

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