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Publisher: | Plume |

Copyright: | 2002, 2003 |

Printing: | May 2003 |

ISBN: | 0-452-28439-2 |

Format: | Trade paperback |

Pages: | 241 |

Barabási at the time of this writing was a professor of physics at Notre Dame University (he's now the director of Northeastern University's Center of Complex Networks). Linked is a popularization of his research into scale-free networks, their relationship to power-law distributions (such as the distribution of wealth), and a proposed model explaining why so many interconnected systems in nature and human society appear to form scale-free networks. Based on some quick Wikipedia research, it's worth mentioning that the ubiquity of scale-free networks has been questioned and may not be as strong as Barabási claims here, not that you would know about that controversy from this book.

I've had this book sitting in my to-read pile for (checks records) ten years, so I only vaguely remember why I bought it originally, but I think it was recommended as a more scientific look at phenomenon popularized by Malcolm Gladwell in The Tipping Point. It isn't that, exactly; Barabási is much less interested in how ideas spread than he is in network structure and its implications for robustness and propagation through the network. (Contagion, as in virus outbreaks, is the obvious example of the latter.)

There are basically two parts to this book: a history of Barabási's research into scale-free networks and the development of the Barabási-Albert model for scale-free network generation, and then Barabási's attempt to find scale-free networks in everything under the sun and make grandiose claims about the implications of that structure for human understanding. One of these parts is better than the other.

The basic definition of a scale-free network is a network where the degree of the nodes (the number of edges coming into or out of the node) follows a power-law distribution. It's a bit hard to describe a power-law distribution without the math, but the intuitive idea is that the distribution will contain a few "winners" who will have orders of magnitude more connections than the average node, to the point that their connections may dominate the graph. This is very unlike a normal distribution (the familiar bell-shaped curve), where most nodes will cluster around a typical number of connections and the number of nodes with a given count of connections will drop off rapidly in either direction from that peak. A typical example of a power-law distribution outside of networks is personal wealth: rather than clustering around some typical values the way natural measurements like physical height do, a few people (Bill Gates, Warren Buffett) have orders of magnitude more wealth than the average person and a noticeable fraction of all wealth in society.

I am moderately dubious of Barabási's assertion here that most prior analysis of networks before his scale-free work focused on random networks (ones where new nodes are connected at an existing node chosen at random), since this is manifestly not the case in computer science (my personal field). However, scale-free networks are a real phenomenon that have some very interesting properties, and Barabási and Albert's proposal of how they might form (add nodes one at a time, and prefer to attach a new node to the existing node with the most connections) is a simple and compelling model of how they can form. Barabási also discusses a later variation, which Wikipedia names the Bianconi-Barabási model, which adds a fitness function for more complex preferential attachment.

Linked covers the history of the idea from Barabási's perspective, as well as a few of its fascinating properties. One is that scale-free networks may not have a tipping point in the Gladwell sense. Depending on the details, there may not be a lower limit of nodes that have to adopt some new property for it to spread through the network. Another is robustness: scale-free networks are startlingly robust against removal of random nodes from the network, requiring removal of large percentages of the nodes before the network fragments, but are quite vulnerable to a more targeted attack that focuses on removing the hubs (the nodes with substantially more connections than average). Scale-free networks also naturally give rise to "six degrees of separation" effects between any two nodes, since the concentration of connections at hubs lead to short paths.

These parts of Linked were fairly interesting, if sometimes clunky. Unfortunately, Barabási doesn't have enough material to talk about mathematical properties and concrete implications at book length, and instead wanders off into an exercise in finding scale-free networks everywhere (cell metabolism, social networks, epidemics, terrorism), and leaping from that assertion (which Wikipedia, at least, labels as not necessarily backed up by later analysis) to some rather overblown claims. I think my favorite was the confident assertion that by 2020 we will be receiving custom-tailored medicine designed specifically for the biological networks of our unique cells, which, one, clearly isn't going to happen, and two, has a strained and dubious connection to scale-free network theory to say the least. There's more in that vein. (That said, the unexpected mathematical connection between the state transition of a Bose-Einstein condensate and scale-free network collapse given sufficiently strong attachment preference and permission to move connections was at least entertaining.)

The general introduction to scale-free networks was interesting and worth reading, but I think the core ideas of this book could have been compressed into a more concise article (and probably have, somewhere on the Internet). The rest of it was mostly boring, punctuated by the occasional eye-roll. I appreciate Barabási's enthusiasm for his topic — it reminds me of professors I worked with at Stanford and their enthusiasm for their pet theoretical concept — but this may be one reason to have the popularization written by someone else. Not really recommended as a book, but if you really want a (somewhat dated) introduction to scale-free networks, you could do worse.

Reviewed: 2018-12-02

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