Rating: 
Summary: Linkages: Everything is Connected!
Review: The author explores the wide variety of social and biological activities that make up the world we live in. Including among these, of course, is the ever ubiquitous World Wide Web. The thread of the book, is his contention that there is a common theory of 'networks' that applies to all of these seemingly disparate fields.
The author draws upon a wide variety of studies and research, including, but not limited to, his own. It is fascinating to read discussions of how Hollywood actors are connected to each other, followed by a discussion of cellular biology, and realize that there is a rich commonality between them in how they connect and behave. The author carefully and methodically takes us through example after example as he explores network theory, and its recent expansion due to new discoveries in the field.
The books is very readable for those with limited experience in math and science. For those that wish to go in more depth, there is a large body of footnotes and references.
Highly recommended for anyone remotely interested in science and the connectedness of the world. Maybe John Muir was right: everything *is* connected to everything else!
Rating: 
Summary: All that glitters is not gold!
Review: I was very excited about this book before I started reading it. By the first few pages I realized I wasn't going to get the detailed insight I was looking for. The author is not technical at all in the book, although his credentials lead one to believe that he will go into some detail. He boasts a lot which I thought was really uncalled for. The research stories are interesting but he doesn't give more. Novice level, for sure.
Rating:
Summary: What's interesting is not new
Review: If you are interested in learning about the theory and methods of analyzing and understanding social networks this is not a good book. The author is new to the area of networks and although he is very enthusiastic he is just a network "newbee"and is very naive. This book is not about networks but about Barabasi's own work and the work of his friends. Barabasi does not discuss network analysis or the state of the art in network research. He is either unaware or fails to acknowledge volumes of research done on networks by mathematicians and social scientists. When he does pay lip service to this prior work his tone is condescending and his characterizations are just plain wrong. I am surprised that this self proclaimed World Wide Web guru was not innovative enough to click the option in Web of Sciences that would include in his searches citations from the social science citation index. I am of course giving him the benefit of the doubt here. If you are interested in another manifestation of a power law or in "scale free networks" this is your book. If you are serious about learning about network theory or doing network analysis this book is a waste of time.
Rating:
Summary: From threads to webs: a broader overview
Review: In all the chapters (author calls "links"- a novel idea for a book on networks!) Barabasi has nicely stitched the captivating overviews on different networking details and offer a general progressing view about network analysis. He used the "World Wide Web network" as a basis for network analysis and extended the findings to other possible real-world networks (cellular-proteins, diseases, network of actors at Hollywood, terrorist networks, web of scientists linked with co-authorship, collaborative web behind economy and so on).
As pointed in introduction, "reductionism" was the buzzword of twentieth century"s research based on the assumption "that once we understand parts, it will be easy to grasp whole" but the reassembly turned out to be harder leading us into "complexity". Barabasi projects the concept to the nodes of a network and shows another angle of the holistic view that was elaborated decades back. He begins with 18th-century Swiss mathematician, Euler and his "bridges" problem, stopping briefly on "graph theory" (a basis for thinking about today"s "networks"), moves on to Erdos (and Renyi) and their random network theory based on "chance" and "randomness" showing static nature of networks. The link between "Six degrees of separation" by Milgram (experimental psychologist) and its preceding probable origin in Karinthy"s (Hungerian writer) fictional story named "chains" (that first conveyed that at the most five acquaintances connect every person on earth) leads to his own findings: "19 degrees of separation" which estimated Internet to be a small sized world in a way that every document is on average 19 clicks away from any other. Moving from Granovetter"s idea that society is a fragmented web of fully connected "clusters" communicating through "weak ties", to "synchronization" and the quantity "clustering coefficient" by Watts and Strogatz, the author paves a way to his own concepts of "hubs and connectors". The few "connectors" in society are those who know a large number of people like Internet architecture is dominated by a few very highly connected nodes or "hubs". Italian economist Pareto["s] 80/20 (80% citations go to only 38% scientists) rule is related here followed by "POWER LAW" with good examples. An important property is connected to hubs - a consequence of power laws: "scale free networks". Other properties about networks are pointed through two laws. The "growth" (i.e. networks grow (addition of new nodes), it is compared with Erdos and Renyi"s static and random networks) and the "preferential attachment" (rich get richer: each network starts from small nucleus and expands with addition to new nodes). An answer to: why hubs and power laws emerge in scale free model? And a foundation for theory of evolving networks. Attaching further advancement - "competition" and that each node has certain "fitness", author assigns "fitness" to each Internet node in a network that mimics its ability to compete for links by showing how an early bird is not necessarily a winner (a tussle between "yahoo" to "google"). "Competition" shapes the network topology - it is matched with "Bose-Einstein condensation". Barabasi categorized the networks into two kinds based on topology of networks. The first, in which scale-free topology survives despite the competition for links (fit get rich behavior): coexistence (of hierarchy) of hubs. Examples encompass real networks. Second is star topology - not scale-free (winner takes all the links) with an example of Microsoft. Stress is on scale-free wins most of the networks. Attention is paid to "robustness of networks" - as in internet, a significant fraction of nodes can be randomly removed from any scale-free network without its breaking apart. Author justifies it with September 11 attacks - why the hubs were targeted? and further explains how the highly infected hubs offer means of persistence and spread based on a large numbers of links connected to it. The examples range from AIDS, to attractive e-mails, to computer virus. Now Paul Baran"s networks (prototype of Internet) find their way with a bit of 1960s-ARPA history. Further, leading to the idea of global internet map, he labels as "success disaster" - a human design that lives a life of its own having underlying fractal structure. The chapter "awakening Internet" concludes with interconnected remarks on growing and evolving Internet at an unparalleled rate paving a possibility towards self-awareness [a parallel to Wolfram"s idea of world being a computer and concept of cellular automata elaborate in his new book "a new kind of science"]. But then where to fit the remotest pockets (say in third world) where social life is still technologically primitive? In chapter "fragmented web", mapping Internet (size wise) is related with search robots capable of indexing and searching the web with reference to search engines. Two chapters "map of life" (genetics) and "network economy" (economics) are good applications but a reservation is made towards a similar hub on "ecological networks". It is enough convincing that Human Genome Project or Celera Genomics have brought in the "the map of life" but a cellular search engine is still absent. Author shows that ATP is a biggest hub in living system by participating in huge number of reactions. Attention on cancer and manic depression are other analogies here. The last chapter "the web without a spider" emphasizes "no global controller" and shows how a scale-free network is a web without a spider. Once again, author talking about September 11 events and networks to other self-organized instances concludes with the question - where do we go from here? His answer is "un-wrapping", "focusing on dynamics that take place along links and moving beyond the structure and topology towards understanding complexity". The author"s aim to get you to think networks: how they emerge, look like and evolve is well received. A chapter or big section on already acknowledged networks in "ecological systems" and attention on meta-networks would have added value to this already enough thoughtful, captivating, easy to understand, well designed rather spun web of dynamic efforts and ideas that this book presents. A last touch: the rich "notes" would be better with a separate bibliography.
Rating:
Summary: How the real world hooks up
Review: Mathematicians have been studying and modelling networks for nearly three centuries, ever since Gauss puzzled out the arrangement of bridges in Koenigsberg. By half a century ago, there was a vast literature of tens of thousands of papers on what had come to be called "graph theory." The big surprise is that no one ever tried to model the growth of networks, or to compare the models with large, spontaneously occurring networks in the real world, until the late 1990s. Barabasi was in on the ground floor of those fledgling efforts, and luckily for all of us to whom real world networks matter (nets of business associates, of friends, of interlocking corporate boards, of Internet nodes, web links, power grids, and half a dozen other major examples to be found here), he happens to have a great gift for clear, lively explanation. Barabasi's style is *crisp*: it crackles with the excitement he feels for this exploding field of study; there's enough real substance for us techies to get our teeth into (rather than skipping the few formulas, he sequesters them in footnotes); he waters down nothing, and still makes everything seem simple. (Partly this is because the subject *is* relatively simple. It's been built from the ground up in just the last few years, so understanding it doesn't require understanding a lot of previously built technical machinery.) It turns out that all the very different real world networks I listed above have, allowing for a certain modicum of randomness, the same life histories, the same adult form, and a penchant for obeying the same laws. First, all of them are "small worlds." The average number of "degrees of separation" for the friendship web in the U.S. is clearly less than 6. The maximum number of links separating two of the 800 million public web pages around in 1998 was under 19. The average distance between any two stars in the Kevin Bacon game is less than 4. And so on. Second, the links aren't randomly distributed at all. There's a "the rich get richer" phenomenon, in which early members of the network acquire more links, and nodes with more links are more likely to acquire new ones. Count and plot the number of links, and you'll get nothing like the familiar old bell curve. Instead it looks like what's called a "power law": the lion's share of links go to a handful of power nodes, which Barabasi calls "hubs" - think Amazon and Google. Most of the rest go to a larger, but still very small, collection of nodes. And so on, down to the vast array of the Johnny-come-latelies and also rans, with their two or three connections to the network. A critical question these days is: how robust are these spontaneously grown networks? How resistant are they to failure? And the reassuring answer is, that even when you remove an enormous number of nodes, a quarter or a third of them, nets like these stay up and working and connected. The less reassuring answer is, that all these networks are highly vulnerable to deliberate attacks. If saboteurs adopt a take-out-the-hubs strategy, removing only a few dozen can reduce a network of millions of nodes to fragments. One of Barabasi's most intriguing examples is the network of chemicals in a living cell, where two chemicals are "linked" if there's some reaction involving both of them. The hubs turn out to be water, and the energy transporters ATP and ADP. But the degrees of separation are atypical among all the networks discussed, which raises some fascinating evolutionary issues. I have only one complaint: I was left thirsty for more detail on everything. And only one quibble: he enthuses at one point that mapping out the biochemical network will make the search for effective drugs a breeze, whereas it seems just as likely that it will make it plain that the search is impossibly hard. After all, many of the classic examples of NP-complete problems come from the older (and, being static rather than dynamic, simpler) realm of graph theory. In an ever more interconnected world, all of us are plugged in to a battery of dynamic networks. So Barabasi's book has vital application to every reader's life. How fortunate that he has made it so easy for every reader to grasp the shape and the rules of the linked life.
Rating:
Summary: Great biblio. for mathematians.
Review: The book assumes almost no mathematical background but it manages to detail a lot of his research processes. One of the best things about the book is the bibliography section which contains a wealth of references to the latest research papers --- if you are interested enough to know about the technical details!
Rating: 
Summary: An explanation
Review: Please skip this review if you are new to this discussion.
I am responding to a review below. I just noticed that a reader from Surrey, UK has challenged my earlier review, particularly my opinion that preferential attachment is overemphasized in the book. Since I have no other method to respond to him, I'll use the review list as a forum. True, I don't have an alternative explanation. My complaint is that networks have properties other than being scale-free--clustering, average path length, etc. The networks generated using preferential attachment, while scale-free, do not tend to match in these properties. (Furthermore, different types of networks tend to differ in these properties, an important fact that wasn't even alluded to in Barabasi's attempt to have a unified networks theory.) Preferential attachment is just one explanation, and a plausible one. In some instances I think it is a real phenomenon. In other instances, I don't believe there is preferential attachment at all. There are some instances where I feel there is preferential attachment in the opposite sense (maybe that is why fads come and go: there is a driving force for people to choose something new after everybody has already seen it). We are clearly missing a crucial piece of the picture. Either that, or network formation is not as universal a phenomenon as Barabasi would like to believe.
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Summary: Of Networks and Men
Review: Every good song has a hook--a line or a melody that makes you want you to hear more. For me the hook that kept me reading "Linked" appears on page 6. "Here is a secret that never makes the headlines" writes the author Barabasi, a professor of physics at Notre Dame, "We have taken apart the universe and have no idea how to put it back together. After spending trillions of research dollars to dissassemble nature...we are just now acknowledging that we have no clue how to continue -- except to take it apart further." Could the prevalent reductionism be, at this point, obscuring rather that sharpening our vision of the the world? Should we give up on the idea of having an equation that could be printed on a tee shirt that contains the Theory of Everything? Barabasi's answer is not to stop analyzing, but rather to analyze certain relatively large self organized systems topologically over time, as if you were reading successive maps of a growing cosmopolitan region. As roads, building complexes, public areas and the like are added, the rules generating the growth and distribution of facilities make themselves plain. The network thus formed works in a manner that obeys certain laws (called power laws)and divides into highly connected places called hubs which link to less numerously connected nodes. Using this analytical approach, he poses and answers dozens of questions that should stimulate an intelligent reader into seeing the world a little differently. In fact, much of this book's interest derives from the activity of charting itself. Barabasi, like some latter day Henry the Navigator, explains in ordinary language why Kevin Bacon is really not very central to the Hollywood map and how the late Rod Steiger was. How Microsoft follows the same laws as a strange sort of matter. He explains how Vernon Jordan got so many corporate board seats, what may be the best way to eradicate AIDs, and what may become the newest phamaceutical approaches to disease. Terrorist networks, cancer and bad economics may all one day fall victim to strategies derived from network analysis. I have read too many popular science books to know that translating equations into popular literature is no easy feat. So many fall of their very lack of weight. My one complaint with this highly readable book is that in an effort to make the book gallop the author has taken some shortcuts which break the flow of the argument. Nonetheless, if you want to read a very good book about a scientific issue not many of us are familiar with, this is a very good one.
Rating:
Summary: I wish all popular science books were this good...
Review: This book is an absolute diamond. I am not an explorer at the very frontiers of network dynamics, but I am an intelligent, sentient being and the ideas being developed in network research are of great importance to my life and the world I live in. I find the academic journals, where the research results are first published, (deliberately?) impenetrable, so it is a delight and a joy to find a guided tour in plain English, with an authoritative guide, through the frontiers of some very current and paradigm-changing ideas. The book's narrative is aimed at the general public, to be sure, but I hadn't heard that being a member of the general public was a crime or a slur. Perhaps I missed a meeting. I found the writing style to be clear, concise, engaging and entertaining. In short, it was one of the best books of any genre that I have ever read and I have read hundreds. Another reviewer of this book (see below) has said that Barabasi overemphasises the importance of preferential attachment in forming scale free network topologies. OK. Bring it on. Where is your counter explanation? What is the more important factor? Where is your clearly-written book explaining your counter argument for the likes of me? I would really like to know what else could possibly account for the emergence of this topology. It's important to me. To that reviewer I say, "publish or be dammed". I cannot abide elitism in scientific research, whereby those in the know jealously guard their secrets from the rest of us, so as to reinforce their self-belief in their uber-mortality. Join the real world. Tells us what you know without being patronising. If, as a reader, you are in any way interested in the spread (or diffusion) of ideas, innovations, fads, viruses, memes, rumours and a hundred other phenomena or want to understand why some things are runaway hits and others not, this book will definitely stimulate your thinking. The only minor frustration I had with the book was that in identifying Microsoft's success in operating systems as analogous to a Bose Einstein condensate (a superfluid?), the book fails to explain how the condensate can evaporate...in other words, what nodal or network conditions would have to apply to overturn Microsoft's dominance? As a published technical author myself, I know that writing this well is sheer hard work. I would be delighted if any of my own works were as brilliantly executed as Barabasi's "Linked". Buy it. You won't be sorry.
Rating:
Summary: To be sure...
Review: This field of network research is undoubtably interesting and important. However, this book suffers from poor writing and a sense of self-importance. I finished it only because of its relevance to my own research, but at times I had to take a break from the narrative. One particularly annoying aspect of the book is that Barabasi frequently uses phrases like "to our great surprise" when describing his own research. The reader can sense the delight that Barabasi takes in pretending that these weren't the results that he was looking for all along. The constant name-dropping and mentioning of his publications in prestigious journals is also irksome. Barabasi continually makes bombastic claims without proof and, in particular, overemphasizes the importance of preferential attachment in creating networks with scale-free topology. Barabasi's writing is bland and repetitive, both in content and in phrasing. For instance, the phrase "to be sure" probably appears 50 times in this approximately 250 page book. Many of the later chapters are almost completely free of scientific content and seem to be added simply for the sake of adding bulk to the book. Barabasi is sweeping a lot of things under the rug with this book, and also ignoring some important recent results in the area of network dynamics. If you get a chance to see Barabasi speak, by all means do so. He's a great speaker, and it's a good way to learn the entire content of this book in only an hour. I can't recommend another book in this field, although Complexity by Waldrop is somewhat related and well-written. To be sure, Barabasi is an important person in this field, but he should stick to his research so that he can get some more publications in prestigious journals.
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