Linked: How Everything Is Connected to Everything Else and What It Means

Rating:
Summary: A fascinating exploration of the laws behind networks
Review: In "Linked" Barabási takes us on a winding journey from the beginnings of graph theory to the new science of networks advocated by him and his peers. This book examines all manner of networks, ranging from the Internet and the World-Wide Web to social and economic networks to gene regulatory networks. What is fascinating about the work conducted by Barabási and others are the "universal laws" that tie these seemingly disparate networks together. This book brings together the work of Barabási's research group and the work of others in the field of network science.

Barabási writes with a clarity that some would argue is rare in works of a scientific nature. He explains his work in clear English, relegating any mathematical equations to footnotes for those who are interested. Power-law degree distributions and their relation to scale-free networks are at the core of his research, and thus he devotes much of the sixth chapter explaining what power-laws are, and how to identify them should you come across one in a plot of your data.

Of course, this is a book for the masses. It is not a text book. As such, he glosses over many of the intricate details and mathematics essential for a deep understanding of scale-free networks and related topics. But the book loses nothing at all by omitting these details. Rather, it allows the reader to see the much bigger picture (the underlying relationships between all kinds of different networks) which was obviously the overriding goal of this book. The book is concluded by more than thirty pages of notes, which the curious reader may peruse to obtain references to scientific publications which examine some of the concepts in much more detail.

The book is slightly repetitive, but this is perhaps inevitable when one considers the central thesis of this book: take any naturally occurring network, and the chances are it is scale-free. Thus, every direction the book takes leads back to scale-free topologies!

I would have preferred to see a little more detail. But that said, Barabási succeeds in his stated goal of taking the reader on a trip across disciplines and challenging the researcher to think in terms of networks.

Rating:
Summary: Book's Audience: Who should be linked to this book.
Review: 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.

Rating:
Summary: Popular Science at its Best
Review: 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.

Rating:
Summary: Reduction to nodes and links
Review: 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 ([e-mail]).

Rating:
Summary: Thought provoking
Review: With so much buzz about Wolfram's book, great to see a book that DOES talk about NEW science. Barabasi, the top guy in the new science of networks, talks about what he knows best: complexity and networks, and how they affect our life. While an easy read, it is full of so many thought provoking ideas, that I'd read for a while and then have to put it down to reflect over the details of what I'd just read. Gladwell's tipping point was an entertaining read, but light on true understanding. Linked makes up the difference: it breaks new ground, offering the reader insight and research into the structure of networks in just about all fields and aspects of life. While Gladwell chats about connectors, people who are incredibly sociable and well-connected, Barabasi is the one who really gets to the heart of the matter. He discovered these connectors (he calls them hubs) while looking at the www (Yahoo and Google are some of those), and he shows that they are present in the cell, in the business world (Vernon Jordan), in sex (Wild Chamberlain), in Hollyood (Kavin Bacon) and many other networks. These hubs are not accidents, but they appear in all networks as a simple rich gets richer process is responsible for them.

If you REALLY want to grasp how ideas spread, how to stop AIDS, how to break down the Internet, how to use your neighbor's computer, how to make your website matter or how to became a board member in a big company, Linked is a good place to start. Barabasi breaks down a complex world into very simple, clear concepts. While I have read several books about 'new' science, this one is really about something new, exciting, and hard to forget. Highly recommend it.

Rating:
Summary: A fascinating exploration of the laws behind networks
Review: In "Linked" Barabási takes us on a winding journey from the beginnings of graph theory to the new science of networks advocated by him and his peers. This book examines all manner of networks, ranging from the Internet and the World-Wide Web to social and economic networks to gene regulatory networks. What is fascinating about the work conducted by Barabási and others are the "universal laws" that tie these seemingly disparate networks together. This book brings together the work of Barabási's research group and the work of others in the field of network science.

Barabási writes with a clarity that some would argue is rare in works of a scientific nature. He explains his work in clear English, relegating any mathematical equations to footnotes for those who are interested. Power-law degree distributions and their relation to scale-free networks are at the core of his research, and thus he devotes much of the sixth chapter explaining what power-laws are, and how to identify them should you come across one in a plot of your data.

Of course, this is a book for the masses. It is not a text book. As such, he glosses over many of the intricate details and mathematics essential for a deep understanding of scale-free networks and related topics. But the book loses nothing at all by omitting these details. Rather, it allows the reader to see the much bigger picture (the underlying relationships between all kinds of different networks) which was obviously the overriding goal of this book. The book is concluded by more than thirty pages of notes, which the curious reader may peruse to obtain references to scientific publications which examine some of the concepts in much more detail.

The book is slightly repetitive, but this is perhaps inevitable when one considers the central thesis of this book: take any naturally occurring network, and the chances are it is scale-free. Thus, every direction the book takes leads back to scale-free topologies!

I would have preferred to see a little more detail. But that said, Barabási succeeds in his stated goal of taking the reader on a trip across disciplines and challenging the researcher to think in terms of networks.

Rating:
Summary: I read this book
Review: I liked it. I read, but I rarely finish a book. I finished this one.

Rating:
Summary: Not for the general reader.
Review: 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.

Rating:
Summary: My expectations were too high
Review: I had received this book from a friend, and my expectations were high, too high.

The book starts good, but it runs out of gas about halfway, when the author keeps repeating the same theories over and over again, just trying to get enough 'search keywords" in his book to get more hits on book search engines.

Also, it is too obvious that it is written by a theoretical scientist, not an observational one. Once he has an idea of how his theory 'should' be, he is just adding enough components and factors to his formula to prove that the reality is exactly like his theory. A scientist should sometimes accept that he's not able to explain what he sees. The author was to proud to get to that stage.

I also agree with some other reviewers that the author does a great job promoting his own accomplishments, 'en passant' slapping in the face of government agencies for not granting him money for his research.

Overall, the book would have been better at about 100 page instead of 226.

I stuggled through the book, but it was a big effort, and I did it only because I received the book and promised to discuss it. If my expectations had been lower, I would have appreciated this more.

I am very interested by the subject, but will have to look for the better books about it.

Rating:
Summary: Good book about networks, but not as good as Nexus
Review: Barabasi's Linked is a pretty good intro to the science of networks. It covers much the same ground as Buchanan's Nexus including discussions about random v. nonrandom networks, six degrees of separation related stuff, the AIDS epidemic, etc. Linked also covers a several more topics in greater detail than does Nexus including viruses and fads, and Barabasi presents very good discussions of search engines and the good-old-boy network of board members. Another appealing aspect of the book is that the author and his co-workers were involved in a number of the developments in the science of networks, so we can be sure that the author's explanations are grounded in his own experience.

If you want to read one book about networks and you're deciding between Nexus and Linked, I would recommend Nexus, however. Nexus' discussions are deeper, and its presentation and writing are better.