Customer Reviews

Linked: The New Science Of Networks Science Of Networks

5 star:		(37)
4 star:		(9)
3 star:		(4)
2 star:		(7)
1 star:		(5)

 

 

I have focused this review on the audience of the book, since other reviews have quite adequately summarized the material.

There have been a lot of books recently that have been published on the new science of networks. Network theory and how it applies to many different fields from technology, marketing, biology, social science, terrorism, disease control etc. (Six Degrees by Duncan Watts, Nexus - Mark Buchanan, Smart Mobs - Howard Rheingold, Tipping Point - Malcolm Gladwell etc..).

Barabasi's is a welcome addition to the field and has a nice niche, which isn't filled by the other books. As some other reviewers have pointed this book is a popular science book, which means it covers scientific and mathematical theories at a very high level and makes these theories accessible to a wide audience. The niche lies somewhere between Gladwell's Tipping Point and Watt's Six Degrees. It is very well written and draws you in with stories that explore the theories. Some of the other reviewers have complained that Barabasi has done a disservice to the theories that he explains by making them too simplistic. I disagree, I actually found this book to be very rewarding, and a quick read, which is a sign of a well-written book. I have never been a fan of scientific and academic books that pride themselves on being totally incomprehensible. Richard Feynman, the Nobel Prize winning physicist, once said that if someone truly understands a subject they should be able to explain it to a general audience without resorting to technical jargon (Feynman's Lectures on Physics Vol 1,2,3 are a perfect example). To be able to explain a complex subject you need to resort analogies, examples and stories. Stories give a framework for the general reader to absorb the complex material. Barabasi has managed to explain the science of networks using all three. I am not sure how this can be seen as a bad thing. This exposes a wider audience to a very interesting subject; this has to be good thing.

Summary:
Anybody who loved Gladwell's Tipping Point and was looking for a book that explains some of the theories behind the phenomena will love this book. It's a little bit more technical than Gladwell's book, but it is well written and it will appeal to a wide audience. As popular science books go, this is definitely on par with Ed Regis's Nano and Steven Levy's Artificial Life, but not quite at the level of Gleick's Chaos. If you are looking for a technical book, you should look at Duncan Watt's Six Degrees, or Small Worlds.

Albert Barabasi presents the lay reader with a stimulating description of the origins of network theory and recent applications. He describes random networks, small world and scalefree networks. In nonrandom networks the importance of hubs is emphasized. Small world networks are the ones with a well defined averge number of links, and in scalefree ones the density of links scales as a power law. For the many interesting examples discussed, I would like to have seen graphs showing scaling over at least three decades in order to be convinced of scaling. However, in practice, whether a network scales or not may not be so important. I liked best the discussions of terrorism, AIDS, and biology. If one could locate the hubs, then a small world network could be destroyed, but as the author points out there is no systematic method for locating the hubs. Also, destroyed hubs in a terror network might be replaced rather fast, whereas airline hubs could not be replaced so quickly. The book might be seen as indicating a starting point to try to develop a branch of mathematical sociology. For example, the maintainance of ethnic identity outside the Heimat is discussed in terms of networking. Now for a little criticism.

I did not find the discussion of ‚the rich get richer' very helpful because network theory at this stage deals only with static geometry, not with empirically-based dynamics. In fact, the dynamics of financial markets have been described empirically accurately without using any notion of networking. In the text the phrase „economic stability" is used but stability is a dynamic idea, and there is no known empirical evidence from the analysis of real markets for any kind of stability. The absence of dynamics on networks means that complexity is not described at all: there is nothing complex about the geometry of a static network! Suggesting that cell biology can be described by networking is empty so long as dynamics are not deduced from empirics. Nonempirical models of dynamics will probably not be of much use for making advances in understanding or treating cancer, e.g. Everything we know about cell biology and cancer was discovered via reductionism, by isolating cause and effect the way that a good auto mechanic does in order to repair a car.

Unfortunately, the author lets his enthusiasm get the best of him when he proclaims „laws of self-organization" and the need to go beyond reductionism. First, there are no known laws of „self-organization". The only known laws of nature are the laws of physics and consequences deduced from the laws, namely, chemistry and cell biology. Worse, every mathematical model that can be written down is a form of reductionism. Quantum theory reduces phenomena to (explains phenomena via) atoms and molecules. All of chemistry is about that. Cell biology attempts to reduce observed phenomena to DNA, proteins, and cells. Believers in self-organized criticality try to reduce the important features of nature to the equivalent of sandpiles. Network enthusiasts hope to reduce phenomena to nodes and links. In order to try to isolate cause and effect, there is no escape from reductionism of one form or another, holism being an empty illusion. So I did not at all like the assertion on pg. 200 that globalization (via deregulation and privatization) is inevitable, because there is no law that tells us that it is.

Summarizng: there is no complexity without dynamics, there are no known „laws of self-organization", and reductionism is the only hope for doing science. Anyone who disagrees with this is welcome to explain to me and others the alternative (jmccauley@uh.edu).

If you read this, you will read about Barabasi's exciting work and the work of his friends. You will read about the risks he and his colleagues take with their careers. You will read about the incredible inertia in academia. But, you won't find much insight into the principles of network dynamics?

I'm not sure the book delivers. We get a 'report from the field', but not much detail or general understanding. It's all too confusing and new, if I caught Barabasi's drift.

But, is this a good 'introduction' to network dynamics? Based on the reviews here, it seems clear the prose appeals to many readers. If this inspires people to read more, then great. I am afraid they are attracted by the comforting tone and soothing outlook, though. We get too much of Barabasi, the expert grant writer. Barabasi foresees network dynamics leading us to Kurzweil's happy 'Age of Spiritual Machines'. A more down to earth view suggests networks bring us Osama Bin Laden. Barabasi is quite thrilled to find small world dynamics in his network research, but never connects them to the 'small world dynamics' of drug lords and suicide bombers.

I'm a bit puzzled by Barabasi's problems with the details. For example, he does a poor job of explaining exactly what a 'power-law' distribution might be, though he uses the term over and over, again. How does one 'find' a power-law in experimental data? Most people have probably gone through much of their lives never seeing a single one! If you find one, will anyone agree with you?

Offering a few examples that one could work with at home would go a long way. For instance, Barabasi talks about the way wealth approximates a power-law distribution. If you try to work with published data on this subjects, there won't be much that looks like a power-law. In fact, the whole idea is rather controversial. It confounds our intuitions and sense of what is right. A power-law distribution of wealth has a few rich, a few more at the 'middle income' level and huge masses in the 'poor' bracket. We would rather have income distributed according to a 'bell curve', a few rich, a few poor and most 'middle class.' If you want to claim 1) natural is 'good', 2) power-laws are 'natural', and 3) wealth has a power-law distribution, why complain about a vanishing middle class? A big middle class is unnatural!

These and other conundrums of the network await the reader's next journey into the subject matter.

In my opinion, Barabasi's book is a decent addition to the existing books on complexity and networks. The author himself is an acknowledged authority of the field of networks. I disagree with some previous critiques that suggest that the book is empty. The book in fact covers a lot of material with just enough technical detail that a layman can understand. The author is able to explain well how various networks form, work, how they fail etc., bringing examples from a wide variety of fields. The text itself is easily readable and provides the reader a good and intuitive understanding of networks without essentially resorting to the language of mathematics which is exactly what Richard Feynman does in his famous book "The character of physical law". If anybody tried to do this, this is not at all an easy task and hence I consider it a nice achievement. Just to mention one particular example, I have not seen such a crystal clear explanation of the strange and somewhat mystic "power law" distribution that shows up in virtually all complex systems and which most books fail to communicate to the public. So if you are looking for an entertaining book which also provides a good and easy introduction into networks at work, you should read it.

This book gives an excellent overview of network science. Being a software engineer and therefore having a significant computer science background, I found this book especially interesting.

Any computer science student looking for something to read in their spare time should definitely consider adding Linked to the must-read list.

This is an excellent book. The author is extremely able to explain difficult concepts about complex systems in a simple and precise manner, using examples from a variety of domains. The richness of applications -- ranging from spread of epidemics to the internet-- is the first strength of the book and of the theory of scale free networks that appear to be a very promising and original tool to understand the web of interactions of complex systems. The second strength is the clarity of writing: a rarity in the scientific world. This book is an example of good writing with the objective of being understood and making science accessible.

I first heard the author speak on NPR. Not only was I enthralled with his intelligence and clarity of thought, I was captivated by the promise of a new perspective on the connectedness of all things, from the sizes of stars in a galaxy to the revolution in internet search engines to the biology of the cell. This book delivers on that promise with insight, wit and style.

Written by a leading researcher in the field, Linked offers ordinary people a glimpse into the fascinating and complex world of networks. The book covers everything from social networks, to economic networks, to chemical networks, to the World Wide Web, and exposes some of the underlying principles responsible for network structure.

Linked explains the importance and prevalence hubs in networks, it highlights the strengths and vulnerabilities of different types of networks and offers insights into such interesting phenomena as the small world effect (best illustrated by the six degrees to Kevin Bacon game).

The book does have its weak spots, and it seems to slow down a bit after about 150 pages. However, the book is divided into very short chapters and is filled with many interesting anecdotes - all this means that even the occasional weak chapter moves along nicely.

All in all, a great and easy to read book for fans of popular science, but be aware that the underlying topic is complex and the occasional equation does find its way into the book. I skipped over most of these equations and found that my understanding of the concepts and my enjoyment of the book were not diminished. I highly recommend this book.

I liked it. I read, but I rarely finish a book. I finished this one.

After reading a third of the book, I finally gave up out of sheer disinterest. The way the book is presented would probably be ideal for a student of network dynamics, or even a mathmetician. For an average reader like myself, it is far too detailed and laborious. There is very little entertainment value in the discussions that surround the occasional revelations. My suggestion: Read the abbridged version.