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A cocktail party? A terrorist cell? Ancient bacteria? An international conglomerate? All are networks, and all are a part of a surprising scientific revolution. Albert-László Barabási, the nation’s foremost expert in the new science of networks and author of Bursts, takes us on an intellectual adventure to prove that social networks, corporations, and living organisms are m A cocktail party? A terrorist cell? Ancient bacteria? An international conglomerate? All are networks, and all are a part of a surprising scientific revolution. Albert-László Barabási, the nation’s foremost expert in the new science of networks and author of Bursts, takes us on an intellectual adventure to prove that social networks, corporations, and living organisms are more similar than previously thought. Grasping a full understanding of network science will someday allow us to design blue-chip businesses, stop the outbreak of deadly diseases, and influence the exchange of ideas and information. Just as James Gleick and the Erdos–Rényi model brought the discovery of chaos theory to the general public, Linked tells the story of the true science of the future and of experiments in statistical mechanics on the internet, all vital parts of what would eventually be called the Barabási–Albert model.  


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A cocktail party? A terrorist cell? Ancient bacteria? An international conglomerate? All are networks, and all are a part of a surprising scientific revolution. Albert-László Barabási, the nation’s foremost expert in the new science of networks and author of Bursts, takes us on an intellectual adventure to prove that social networks, corporations, and living organisms are m A cocktail party? A terrorist cell? Ancient bacteria? An international conglomerate? All are networks, and all are a part of a surprising scientific revolution. Albert-László Barabási, the nation’s foremost expert in the new science of networks and author of Bursts, takes us on an intellectual adventure to prove that social networks, corporations, and living organisms are more similar than previously thought. Grasping a full understanding of network science will someday allow us to design blue-chip businesses, stop the outbreak of deadly diseases, and influence the exchange of ideas and information. Just as James Gleick and the Erdos–Rényi model brought the discovery of chaos theory to the general public, Linked tells the story of the true science of the future and of experiments in statistical mechanics on the internet, all vital parts of what would eventually be called the Barabási–Albert model.  

30 review for Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life

  1. 4 out of 5

    Trevor

    I liked this very much. The main thesis is that science up to fairly recently has been Platonic (which this book instead, and I think mistakenly, characterises as reductionist) and therefore fixated on describing things and their forms. This idea is that if you have a picture you want to study you will learn all that there is to learn about it by pulling all of the jigsaw pieces apart and studying these individual pieces in detail. As String Theory shows, we can always speculate on smaller and s I liked this very much. The main thesis is that science up to fairly recently has been Platonic (which this book instead, and I think mistakenly, characterises as reductionist) and therefore fixated on describing things and their forms. This idea is that if you have a picture you want to study you will learn all that there is to learn about it by pulling all of the jigsaw pieces apart and studying these individual pieces in detail. As String Theory shows, we can always speculate on smaller and smaller component parts, but it is not clear that gaining a detailed knowledge of all of these parts will inevitably tell us all there is to know about how these parts work in unison. The author makes it clear that he views that the path of science will be away from what he calls reductionism (and I would call a Platonic obsession with ‘things’) towards a deeper understanding of how these components already more or less described in detail work together in networks of relationships to bring about complex and emergent behaviours and phenomena. I have a fundamental faith that any view that turns our attention away from ‘things’ and towards relationships is pointing us in the right direction. He uses a very broad palette here to make his point, taking examples from computer science, biology, economics and sociology to build a fascinating case for the role played by networks in assisting our understanding of how the world works. He also makes some fascinating points regarding the development of network theory and how that development has been away from notions of randomness towards much more highly structured and law driven networks. Sorry, that wasn’t clear. He spends a lot of time in this book discussing in very clear prose the problems which have confronted mathematicians when they have sought to describe networks. The earliest models of networks assumed that the links between nodes on the network were more or less random. What has been found since is that networks follow power laws in which they tend to follow Matthew 13:12 “For whosoever hath, to him shall be given, and he shall have more abundance: but whosoever hath not, from him shall be taken away even that he hath,” Much of the sociological implications of networks is much the same as is discussed in Malcolm Gladwell’s The Tipping Point. But you still may want to read this even if you have read that book, as this does give much more background to network theory and therefore helps to make more sense of some of the conclusions drawn in Gladwell’s book. Also, the examples drawn from other sciences, not least computer science, gives an interesting insight into the growing importance of network theory in understanding the world. In a previous life I would have had a better understanding of power series and therefore a deeper understanding of how networks are shown to be less random and more law driven – but in this book such an understanding of mathematics is not assumed nor needed to follow the argument. (Was that a collective sigh of relief I could hear?) At no time did I feel like I was looking down over the abyss of my mathematical ignorance and thinking, “God, if only I’d stuck at it I might even be able to follow what this guy is on about”. He is always clear and makes no assumptions of the reader’s numeracy or intelligence, other than that the reader possessing some threshold level of literacy. And, to be honest, even this wasn’t set too high. There was also a very interesting discussion and explanation of the Pareto Principle which I think in itself made the book worthwhile. This is the rule that one hears far too often from people who have an Masters of Business Administration (or a masters of bugger all as a friend of mine refers to them). The notion that we get 80% of our sales from 20% of our customers being the MBAs Pareto Relation of choice. He says that this rule is not as all pervasive as MBAs would have us believe. Rather, it only is the case in specific situations and this was the most interesting thing in the book, for me. Generally, we would expect things to be ordered around a normal distribution – with height, for example, there are lots of average height people, but far fewer very tall or very short people. The Pareto Principle instead follows a power rule and, as he points out, applies when a system is moving from randomness to an organised state. I would love to read more about this, but this was the first time I have heard someone talking about this relation and I didn’t think – Well, so what? What was most interesting about this book, though, was what was not talked about. He talked about computer networks, he talked about the network relationships within plant and animal cells, but what wasn’t mentioned at all throughout the book (and I expected to hear about it at any moment) was a discussion of that most intriguing of networks, the neural networks in the brain. I wonder if this is because how we describe these neural networks is generally with reference to computer, highway or other human made networks and the metaphor doesn’t really work going the other way around. There is lots to think about in this book – and like I said, given that it moves us some way from Plato’s world of forms towards notions that everything is connected to everything else makes this book worth reading. I think it is clear that these connections, impulses and directings and how they are played out when one set of a web of interactions impacts upon other parts of that web are worth both our notice and our study.

  2. 5 out of 5

    Gendou

    One of those anti-reductionist, complexity-obsessed, nonsensical collections of persuasive anecdotes and loose (useless) analogies. The main critique of reductionism is that it not always useful. Some problems can't be easily solved from 1st principles. The author points out the solution would be a departure from reductionism. But this straw-man strict reductionist doesn't exist in the first place. Rocket scientists don't model engines on the quark-scale! Barabasi works hard to hide the freedom and ut One of those anti-reductionist, complexity-obsessed, nonsensical collections of persuasive anecdotes and loose (useless) analogies. The main critique of reductionism is that it not always useful. Some problems can't be easily solved from 1st principles. The author points out the solution would be a departure from reductionism. But this straw-man strict reductionist doesn't exist in the first place. Rocket scientists don't model engines on the quark-scale! Barabasi works hard to hide the freedom and utility of model-dependent realism. Topics discussed: * 6 degrees of separation * Almost everything from the book Sync * Power laws * Renormalization (actually a quite good overview) * Phase transitions * Scale-free network topology * Internet search engines (for n00bs, very out of date and superficial) * Internet networking (for total n00bs) Barabasi shows no reserve in abusing nonsense words like "order" and "complexity" outside any mathematically defined context. I laughed out loud when he asks, in all seriousness, "when will the internet become self-aware?" as though it was only a matter of time. Oh, no, not another singularity nutcase! His thesis applied to the web uses an outdated idea of a web "document". Now a days, the web is made up of "apps", and the "document" is a rarity if not altogether unimportant. This book contains a lot of exaggerations and outright false claims to the end of defending the thesis, which is that network theory is NECESSARY for understanding certain systems. For example, "the behavior of living systems can seldom be reduced to their molecular components". This is a disconcertingly ambiguous statement, but if taken at face value, it seems to imply that "behavior of living systems" cannot be described bottom-up from the "molecular components". Molecular biology is a hugely important and productive field in biology which does just this all the time! What I understand the author truly means to argue is that biological problems take a lot of work to solve. There is no single gene for bipolar disorder, for example. Any study where you attempt to find the genetic cause of the heritability of bipolar disorder will involve many tests and lots of data on lots of genes. The steps of scientific reasoning will be voluminous, involved, and the results diluted by huge uncertainties. But slapping the words "genetic network" on the problem is a meaningless extra step. Using fetishist terminology doesn't make the solution to the genetic origin of bipolar disorder any easier. It's a nice framework for talking about abstract high-level concepts, but it's hardly the groundbreaking and necessary future of bio-technology that he author claims. The grandiose presentation in this book is a turn off to me, and the thesis is, to a computer scientist like myself, a lot of hoopla. Read this book if you enjoy listening to a semantics-obsessed bandit taking pot-shots at the proverbial bandwagon and peddling feel-good new-age verbiage.

  3. 5 out of 5

    Jimmy Ele

    Supremely interesting book that delves into network theory and how it's understanding and growth in every branch of science from Biology to Computer Science and Economics will undoubtedly change the way we view the world. It is very exciting to be alive during this time in which the underlying mathematical laws that govern networks are being revealed. Being a student of complexity ever since I read Nassim Nicholas Taleb's book "Antifragile: Things that Gain from Disorder" and followed it up with Supremely interesting book that delves into network theory and how it's understanding and growth in every branch of science from Biology to Computer Science and Economics will undoubtedly change the way we view the world. It is very exciting to be alive during this time in which the underlying mathematical laws that govern networks are being revealed. Being a student of complexity ever since I read Nassim Nicholas Taleb's book "Antifragile: Things that Gain from Disorder" and followed it up with "Complexity: The Emerging Science at the Edge of Order and Chaos" by Waldrop, M. Mitchell I was quite simply enamored by this book (although some people might find certain parts long winded). The names of the chapters are very interesting and serve as great attention hooks to keep you reading. I do not like getting too into my reviews of books, especially if it will take a long summary for people to understand only a watered down version of the book. I will leave this review off with the advice that if you are interested in network theory or complexity then this is a must read. Below you will find a list of books that I have recently read and recommend if you are like me and have been on a search for good books on complexity or any of complexity's related subjects. "The Black Swan: The Impact of the Highly Improbable" by Nassim Nicholas Taleb, "Bursts: The Hidden Pattern Behind Everything We Do" by Albert-Laszlo Barabasi, "The Synaptic Self: How Our Brains Become Who We Are" by Joseph Le-doux, "The Tipping Point: How Little Things Can Make A Big Difference" and "Outliers: The Story of Success" by Malcolm Gladwell, Manuel Lima's gorgeous "Visual Complexity: Mapping Patterns of Information", and "The Birth of the Mind: How a Tiny Number of Genes Creates the Complexities of Human Thought" by Gary Marcus. Currently reading "The (Mis)Behavior of Markets" by Benoit B. Mendelbrot, As well as Lewis Mumford's 2 volume "Myth of the Machine: Technics and Human Development". I intend to follow it with "Hidden Order: How Adaptation Builds Complexity" by John Holland, and "The Nature of Technology: What it is and How it Evolves" by Arthur W. Brian. Happy reading! : )

  4. 5 out of 5

    Kayson Fakhar

    Awesome book if you want to know why should we think in networks

  5. 4 out of 5

    Rachele

    This is great stuff. A very sexy topic as far as physics is concerned. And while that may be just a cliche description that I'm fond of using- sex is actually a relevant topic in the field of networks. Did you know that a sexual network has the same topological structure as the world wide web? Well it does! Prostitutes are like google and your personal website is probably like a virgin. Anywho, while the content is extremely interesting, if you have any prior knowledge of networks, you might fin This is great stuff. A very sexy topic as far as physics is concerned. And while that may be just a cliche description that I'm fond of using- sex is actually a relevant topic in the field of networks. Did you know that a sexual network has the same topological structure as the world wide web? Well it does! Prostitutes are like google and your personal website is probably like a virgin. Anywho, while the content is extremely interesting, if you have any prior knowledge of networks, you might find the book somewhat longwinded. Or you might just find it that way period. I've noticed that everybody else on goodreads who has this book has it either on currently reading or to-read shelf... And I'm not half way done with it yet either. I do want to applaud his efforts at regularly giving short-outs to his grad students. He does a lot of name dropping in this book, but mostly in a good way to people who deserve it. Other than that, all I have to say so far is that (SPOILER ALERT!!!) Chapter 8 is gonna blow your mind when you find out that Bill Gates is a Bose-Einstein Condensate! OMG!

  6. 4 out of 5

    Lawrence

    A very well-written exposition of network theory for a general audience, with extensive end-notes where the author has hidden some of the math. It deals not only with the ideas of networks but also the mathematicians and scientists who study them, resulting in some appealing anecdotes. Beginning with Euler and his 7 bridges of Königsberg problem which gave birth to graph theory, Barabási follows the development of ideas about the nature of social relation nets, the structure of the internet, as A very well-written exposition of network theory for a general audience, with extensive end-notes where the author has hidden some of the math. It deals not only with the ideas of networks but also the mathematicians and scientists who study them, resulting in some appealing anecdotes. Beginning with Euler and his 7 bridges of Königsberg problem which gave birth to graph theory, Barabási follows the development of ideas about the nature of social relation nets, the structure of the internet, as well as the WWW, economic interchanges, neural nets and the all-important 6 degrees of separation paradigm. The copyright is 2003 and, with the rise of "Big Data" in the past 10 years , thinking about networks has certainly moved beyond what is in this book, but it could still serve as a good foundation. For me this kind of book stimulates thinking about life, society, economy, and mind in new and enlightening ways, some of which will probably benefit my art-making (hmmmm, I wonder what will happen if I set up these electronic sculptures so they detect each other's light flashes and respond to them?)

  7. 4 out of 5

    Nathan Scarborough

    This was incredible. I want to pursue this field full time, professionally after reading this book.

  8. 5 out of 5

    Charlene

    I would like to see an updated edition of this book come out soon, one that includes the latest research in protein, gene, and microbiome networks. In the first few chapters, the author guides the reader through the early decades of research in complexity. When networks were first realized, their connections were thought to be random. However, power laws were discovered to be involved in the emergence of every self organizing system. This was a thrilling insight that has held up in subsequent fin I would like to see an updated edition of this book come out soon, one that includes the latest research in protein, gene, and microbiome networks. In the first few chapters, the author guides the reader through the early decades of research in complexity. When networks were first realized, their connections were thought to be random. However, power laws were discovered to be involved in the emergence of every self organizing system. This was a thrilling insight that has held up in subsequent findings. This means that social networks, personal relationships, protein interactions, economics, gene interactions, cell communication, and so on all work in the same way. Thus research in systems science/networks/complexity/emergence studies (whatever name you want to call it) has been able to uncover fundamental laws by which the world and universe at large operate. That is what makes this book and other like it as important to read as books on the theory of gravity, evolution, heliocentrism, or other truths of nature. This book might be too elementary for some people who already understand networks and the maths behind them. However, it is still a great read because it is a reminder of how everything is connected and how that presents wonderful problems for humans to solve. We cannot understand disease, economics, behavior, evolution, the cosmos, and so on without trying to understand the underlying networks that connect things together. My favorite chapter was the chapter on cell, protein, and gene networks. I love how this field has exploded since this book was written. Just this morning, I read a short article about protein networks that reminded me of what I read in this book: http://phys.org/news/2015-09-scientis...

  9. 4 out of 5

    Jason Griggs

    This book has a lot of interesting information about the structure of the Internet. Unfortunately, it was poorly written. It reiterates simple points and fails to spend enough time explaining the complex points. The author seemed to have in mind certain phrases that had to appear in the book and includes these and strange metaphors in places where they don't fit. It also goes off on too many tangents about the publication process of university professors. I listened to the book on CDROM, and it This book has a lot of interesting information about the structure of the Internet. Unfortunately, it was poorly written. It reiterates simple points and fails to spend enough time explaining the complex points. The author seemed to have in mind certain phrases that had to appear in the book and includes these and strange metaphors in places where they don't fit. It also goes off on too many tangents about the publication process of university professors. I listened to the book on CDROM, and it was read by someone who paused in strange places and placed incorrect emphasis, which further confused me. The content is almost entirely about the growth of the Internet and possible ways for a network like the Internet to break down. The book contains a little information on ecological systems too. For me, it was fascinating enough to warrant plodding through it, but I wouldn't recommend it for anybody who is not very interested in mathematics and also interested in academic politics.

  10. 5 out of 5

    Troy Blackford

    This is a very interesting and extremely in-depth look into the science of networks - anything from 'who actors have worked with,' to 'computer networks,' to good ol' real life 'analog' social networks (i.e. 'who you know, and who they know'). Basically, anything with nodes connecting to other things. This book looks at the science of networks primarily from a 'mathematical model' perspective, and as such it was frequently beyond my comprehension. Indeed, though this book was engaging and covere This is a very interesting and extremely in-depth look into the science of networks - anything from 'who actors have worked with,' to 'computer networks,' to good ol' real life 'analog' social networks (i.e. 'who you know, and who they know'). Basically, anything with nodes connecting to other things. This book looks at the science of networks primarily from a 'mathematical model' perspective, and as such it was frequently beyond my comprehension. Indeed, though this book was engaging and covered a variety of topics from financial crises to power-grid meltdowns, the fact that I struggled with the perspective made it seem drier than it was. Someone who feels more at home looking at the world from a mathematical perspective than I myself do wouldn't have that issue. A solid and interesting book of fascinating facts that would likely appeal to the mathematically inclined, or anyone who is interested in knowing more about networks.

  11. 5 out of 5

    Akash Goel

    This review has been hidden because it contains spoilers. To view it, click here. The book is very dense in its information content. The main takeaways are, however, really small. 1. Networks in the real world are not random and follow power laws. 2. A large majority of nodes are sparsely connected. But a small minority of them have a disproportionately high degree of connectivity. Such highly connected nodes are called hubs. 3. Networks such as these are incredibly resistant to breakdowns from random node failures. However, taking down a few carefully selected hub nodes can sp The book is very dense in its information content. The main takeaways are, however, really small. 1. Networks in the real world are not random and follow power laws. 2. A large majority of nodes are sparsely connected. But a small minority of them have a disproportionately high degree of connectivity. Such highly connected nodes are called hubs. 3. Networks such as these are incredibly resistant to breakdowns from random node failures. However, taking down a few carefully selected hub nodes can spell the end for networks. 4. Some networks allow a few nodes to grow so big that they can virtually take over the entire ownership of the network. This is called a winner takes all behaviour, such as shown by MS Windows in the OS marketplace. 5. New entrants can become more connected than older nodes of the network if they have a better fitness which makes them a preferable connection to the old nodes. Hence, first mover nodes have an advantage but not a monopoly on the network. E.g. Google. 6. And finally, networks theory can be used to predict and study a lot of phenomenon ranging from molecular interactions in cells, gene expression, viral epidemics, marketing, economies, political scandals and obviously the Internet. The applications are virtually endless.

  12. 4 out of 5

    Vladimir Stozhkov

    A good non-fiction book for people who would like to be introduced to network science without learning rigorous mathematical formulae. Also, this book can be useful for network science researchers in order to formulate their scientific ideas about network science in laymen terms.

  13. 5 out of 5

    Alejandro V. Betancourt

    Fantastic read on network theory. Most of the concepts aged well, but some of the examples and experiments are outdated. Looking forward to reading more recent works from Barabási.

  14. 4 out of 5

    Ankita Kumari

    It was illuminating to understand the application of network in so many different domains. The book starts off sharp and focused but loses some momentum towards the tail end and is more verbose than necessary.

  15. 5 out of 5

    Robert

    This is an excellent read. It isn't filled with much technical speak and is written in a very easy to read manner. The flow of the book is also very good. I found this book far more enjoyable than 'Sync' which I found hard to follow at times, even though both books deal with similiar subject material. Barabasi has created something here that anyone can read and understand. In summary the book looks at network theory and the discoveries that have been made recently that change the manner in which w This is an excellent read. It isn't filled with much technical speak and is written in a very easy to read manner. The flow of the book is also very good. I found this book far more enjoyable than 'Sync' which I found hard to follow at times, even though both books deal with similiar subject material. Barabasi has created something here that anyone can read and understand. In summary the book looks at network theory and the discoveries that have been made recently that change the manner in which we consider all sorts of networks are constructed. Barabasi shows how networks like the Web are created based on link popularity and how the Web is not a random place at all as most people believe. He also explains why only 40% or so of the Web is actually indexed by search engines and even though the Web is a great place to post your information the chances are that it makes not difference if it is there or not unless it is linked. His notion of scale networks and hub is extremely compelling and interesting. If you are interested in networking in nature or man made then this book is for you. It is extremely well written, easy to understand yet totally engaging. Highly recommended.

  16. 5 out of 5

    Arin Basu

    This is an excellent introduction to the world of social network analysis. Very easily written for an introductory audience and introduces all the essential concepts, yet an excellent treatise on the more intricate and state of the art issues around social network analysis. It's always a pleasure to read firsthand accounts from the authors of the power-law distribution in social networks, the issues around growth models, and preferential attachments. The book goes over a range of issues, startin This is an excellent introduction to the world of social network analysis. Very easily written for an introductory audience and introduces all the essential concepts, yet an excellent treatise on the more intricate and state of the art issues around social network analysis. It's always a pleasure to read firsthand accounts from the authors of the power-law distribution in social networks, the issues around growth models, and preferential attachments. The book goes over a range of issues, starting with the history of social network analysis and Erdos-Renyi random network theory, then walks through Duncan Watts-Strogatz clustering before introducing the hub model that they came up with. The story is told with a gentle pace, and some sections will keep you awake. Extremely well written and a pleasure to read. Plus excellent collection of references and citations that too nicely presented at the end of the book. A treasure really, for all who enjoy these sort of books. Nice, fun to read, easy, yet gently holds your hands as you enter the complex world of social network analysis. Just too good a book!

  17. 5 out of 5

    Christina

    This took me a long time to finish. It was hard to stay interested, especially when they were talking about the internet. Even though the book isn't that old, it felt quite dated. I get that the early days of the internet were exciting in figuring out how the networks worked, but they kept sounding really surprised that some web pages have more links to them than others, a fact to which any person NOT entrenched in the network theory mindset would have said, "yeah, well, duh." I was most interes This took me a long time to finish. It was hard to stay interested, especially when they were talking about the internet. Even though the book isn't that old, it felt quite dated. I get that the early days of the internet were exciting in figuring out how the networks worked, but they kept sounding really surprised that some web pages have more links to them than others, a fact to which any person NOT entrenched in the network theory mindset would have said, "yeah, well, duh." I was most interested when the network theory talk turned to people, like how the most effective way to curb AIDS would be to prioritize treatment for the hubs, a.k.a. the people who sleep with the most people, even though no one would ever go for that idea. Or how things are more than the sum of their parts - it's not just the P53 gene that affects cancer, it's the P53 network. It's how everything works TOGETHER that matters. Like how a carrot is better for you than a pill that has all the vitamins of a carrot.

  18. 5 out of 5

    Anthony

    Unfortunately, too broad an overview to leave the reader with much of anything. Cursory explanations of a graph's constitutive elements, of power laws, and hub-and-spoke models are the extent to which this book actually dives into its own subject matter. The rest of the book is devoted to nothing more but relentlessly hammering in the idea that networks are, like, everywhere, man. Important topics – such as why certain network architectures are more apt for certain cases than others – and key co Unfortunately, too broad an overview to leave the reader with much of anything. Cursory explanations of a graph's constitutive elements, of power laws, and hub-and-spoke models are the extent to which this book actually dives into its own subject matter. The rest of the book is devoted to nothing more but relentlessly hammering in the idea that networks are, like, everywhere, man. Important topics – such as why certain network architectures are more apt for certain cases than others – and key concepts – of centrality or robustness, for instance – would certainly be accessible to a general readership and are essential to a deeper understanding and appreciation of networks, but here they are not given the light of day. A suitable first encounter to network theory, perhaps, for someone who is entirely new to the topic, but this is also the last time I take book recommendations from a stranger in a café.

  19. 5 out of 5

    David

    An engaging, well-written, highly accessible account of the theory behind networks, and the growing importance of this theory in the modern world. Barabasi could serve as a role model for all aspiring science writers - this fascinating book takes a difficult subject and renders it accessible to non-expert readers. To quote 'The Boston Globe': " Linked should be mandatory reading for academics as a primer in good writing. Barabasi may be a scientist, but he didn't neglect his liberal arts educati An engaging, well-written, highly accessible account of the theory behind networks, and the growing importance of this theory in the modern world. Barabasi could serve as a role model for all aspiring science writers - this fascinating book takes a difficult subject and renders it accessible to non-expert readers. To quote 'The Boston Globe': " Linked should be mandatory reading for academics as a primer in good writing. Barabasi may be a scientist, but he didn't neglect his liberal arts education; his Renaissance man's curiosity roves across history, economics, medicine, and pop culture. He writes in understandable lay-speak glittering with wit." (Despite the atrocity of 'lay-speak', their assessment is on the mark).

  20. 4 out of 5

    Dr. Barrett Dylan Brown, Phd

    Interesting enough, though repetative. A pop-cultural textbook for very complicated mathematics/statistics, but never-the-less very relevant and very interesting. The first half of the book builds the groundwork for the information explained in the second half, though for the most part the book just repeats the same concepts over and over (maybe needed for something so compicated). To be honest I already had intuitively come to some of the same conclusions these mathemeticians and physicists came Interesting enough, though repetative. A pop-cultural textbook for very complicated mathematics/statistics, but never-the-less very relevant and very interesting. The first half of the book builds the groundwork for the information explained in the second half, though for the most part the book just repeats the same concepts over and over (maybe needed for something so compicated). To be honest I already had intuitively come to some of the same conclusions these mathemeticians and physicists came to through equations and graphs; interesting to know there is already a vocabulary for phenomena I have noticed. For example I call my "Hub"-friends "Conectrixes." Though "hub" works just as well.

  21. 4 out of 5

    Laurent De Serres Berard

    Would have given 5 stars, if it wasn't some parts a bit repetitive at the end. Simple, but amazing books that make the holistic, universal aspects of networks and their principles accessible to everybody. Written in the beginning of 2000s, you can actually see how he accidentally predicted many aspects of the digital, online economy, and how democracy could be affected by online news through those ''hubs'' of information. For me, i was fascinated because i could saw how this could be applied to Would have given 5 stars, if it wasn't some parts a bit repetitive at the end. Simple, but amazing books that make the holistic, universal aspects of networks and their principles accessible to everybody. Written in the beginning of 2000s, you can actually see how he accidentally predicted many aspects of the digital, online economy, and how democracy could be affected by online news through those ''hubs'' of information. For me, i was fascinated because i could saw how this could be applied to social sciences as well, and the importance of exchange of information.

  22. 4 out of 5

    Javaughn Lawrence

    This is a great introduction to network science. Barabasi provides an overview of the fundamentals of network science, covering random network theory, scale-free networks and the role of hubs. He combines this with detailed illustrations of various phenomena, such as the spread of Christianity, the propagation of cancer in cells and the spread of computer viruses. I highly recommend this for anyone any remote interest in

  23. 5 out of 5

    Julie

    Similar to "The Tipping Point" -- it's more academic and uses examples beyond social settings, and takes some of the same ideas further in more depth. Not quite as accessible as The Tipping Point, but also more realistic and less 'romanticizing' of the science.

  24. 5 out of 5

    Andreea

    Sharp logic and good writing, backed up by sound proof.

  25. 4 out of 5

    Eustacia Tan

    So, I had to read this for a class, which meant that I took notes on every single chapter. So... my review really just consists of my notes. So this review is really just more for me to remember things than for anyone else. First line: February 7, 2000 should have been a big day for Yahoo. The First Link: Introduction - the book opens with the story of mafiaboy, the teenager that managed to bring down Yahoo. Then it changes, quite inexplicably, to Christianity and gives everything to credit. I rea So, I had to read this for a class, which meant that I took notes on every single chapter. So... my review really just consists of my notes. So this review is really just more for me to remember things than for anyone else. First line: February 7, 2000 should have been a big day for Yahoo. The First Link: Introduction - the book opens with the story of mafiaboy, the teenager that managed to bring down Yahoo. Then it changes, quite inexplicably, to Christianity and gives everything to credit. I really don't know if the author simply hasn't read the Bible, or if he's doing one of those things where people pretend that Jesus didn't say the things he said he did. But whatever, the point of this chapter is that the book is about Networks. The second link: The Random Universe - we have Euler (truly an amazing guy) to thank for the graph theory, which is the basis of how we think about networks. Also, the most brilliant mathematicians are the most eccentric. And from Erdos and Renyi, if we have just an average of one link (connection) per node (e.g. Person), then we have a cluster in which everyone is connected. The Third Link: Six Degrees of Separation - this concept really emphasises that we live in a small, dense world. Also, I'm starting to get the hang of the story -> concept structure of the book. Interesting, when the author did a study of the web in 1998-1999, the web had 800 million nodes, but it's diameter was only 18.59, aka around 19 degrees of separation. So in other words, the interconnected nature of the network leads to relatively short paths. Also, 6 degrees of separation may be an overestimation because we don't know everything about our acquaintances and may miss the most efficient route. The Fourth Link: Small Worlds - The story of weak ties. I remember Dayre-ing about this before, but to quote: In "The Strength of Weak Ties" Granovetter proposed something that sounds preposterous at first: when it comes to finding a job, getting news, launching a restaurant or spreading the latest fad, our weak social ties are more important than our cherished strong friendships. Ok, so this chapter is about clustering. Basically, it's an improvement on the Network model by Erdos and Renyi, in that it accounts for both strong and weak ties, making it much more realistic. The Fifth Link: Hubs and Connectors - This introduces the idea of hubs, which will be familiar to you if you've read Malcolm Gladwell's The Topping Point. The chapter, however, ends by admitting that hubs seem to be a mystery and that they challenge the status quo, so I guess that's the next topic. The Sixth Link: The 80/20 Rule - From the Pareto rule, we go into the power law, which is related to hubs (hubs being the thing that early network theories said didn't exist). So the question is, why does the power law indicate order coming out of chaos? I don't know, so on to Chapter 7. The Seventh Link: Rich get richer - The author makes a momentous discovery, writes paper in 10 days. Paper is rejected, but he manages to change editor's mind. Ok, the actual substance of the chapter is Real networks are governed by two laws: growth and preferential attachment. Basically, as the network grows, nodes prefer to form links with other nodes that have many links. I wonder if this is how the first mover's advantage works? I mean, the book calls it "rich get richer", but it sounds like first mover's advantage to me. Something to look into Oh, and the answer to the question in the previous chapter? The power law isn't about turning chaos into order. It's about "organising principles acting at each stage of the network formation process". The Eighth Link: Einstein's Legacy - the book anticipated my question, and here, it tackles first mover's advantage. Google is the first case study here - being a success story that was late to the game. Well, to cut the long story short, it's not timing, but 'fitness' (how useful they are, to put it another way), that determine which nodes become hubs. Then there's something about the Bose-Einstein equation, which is quite tough, but the consequences are: "the winner can take all" The Ninth Link: Achille's Hill - natural systems can withstand high error rates, unlike man-made systems. Except the internet and any network generated on the scale-free network. Apparently hubs keep networks robust? At any rate, error tolerance is a good thing, but it does mean we are vulnerable to attacks (kinda contradictory, imo). The Tenth Link: Viruses and Fads - what does the spread of AIDS have to do with going viral on the Internet? Both are things that spread over the network. From the Pfizer study, innovations spread from innovators to hubs to the masses, like the product life cycle. Then there is the threshold model, which is the level of evidence we need to accept something new. And if we figure out the critical threshold, we can figure out if the innovation will succeed. The Eleventh Link: The Awakening Internet - This is kinda like a mini history of the Internet. If you want a more comprehensive look, definitely read Masterswitch by Tim Wu The Twelfth Link: The Fragmented Web - Continuing from the previous chapters, the Internet is fragmented into smaller communities, which makes sense if you think about it. But it's really hard to find those communities (for researchers). The Thirteenth Link: The Map of Life - On genetics and networks. To be honest, I don't understand much of it, but basically (I guess) everything is a network. The Fourteenth Link: The Network Economy - I love the title of this. Totally looking forward to reading it! And guess what? It turns out that the "old boy's network" is created by small world dynamics. Also, cascading failures (e.g Asian financial crisis) are because of network economies Networks do not offer a miracle drug, a strategy that makes you invincible in any business environment. The truly important role networks play is in helping existing organisations adapt to rapidly changing market conditions. Ok, this is very timely. It's easy to see the Network as a panacea, and that as long as we know that it exists, we will succeed. But, as the quote says, the Network isn't necessarily the end, it may only be the means to the end. The Last Link: Web Without a Spider - to sum: there's a hierarchy of hubs that keep networks close together. But, we haven't completely understood the network yet. Hopefully, though, we can in the future. And the book is more or less done. It was an interesting book, but I can't think of anything to say about it that doesn't involve me summarising it. Like, the book anticipated my questions, so... There's not much I can say. And that does sum up the entire review. This review was first posted at Inside the mind of a Bibliophile

  26. 5 out of 5

    Christian Euler

    Linked is written in an engaging way, and the ideas are all simplified well for laypeople. I particularly liked that it was structured in the same way as the networks it describes -- this is smart, and subtle (though maybe lost on a good chunk of its audience). I would definitely recommend it for someone who has little to no experience working with or understanding complex networks. People in business in particular could probably get something out of it. However, the most glaring feature of this Linked is written in an engaging way, and the ideas are all simplified well for laypeople. I particularly liked that it was structured in the same way as the networks it describes -- this is smart, and subtle (though maybe lost on a good chunk of its audience). I would definitely recommend it for someone who has little to no experience working with or understanding complex networks. People in business in particular could probably get something out of it. However, the most glaring feature of this book is how outdated it is. Like all pop science books, its objective is to sell an idea, and it achieves that objective through claims that it this idea is The Thing that Will Solve All Problems. In 2002, when Linked was first published, it probably did seem like complex network theory could impact almost every aspect of the world in positive ways because of recent advances in the field; however, claiming then that personalized medicine would be widespread by 2020, for example, was a stretch. Indeed, much of the biology section is oversimplified and superficial. Barabasi uses this section to very ironically criticize reductionism, wihout which the very network maps he studies would not exist! He does not develop this critique further than a few sentences which culminate in the vague concept of synergy (ha!), and thus appears to have a weak foundation in theories of knowledge. I think I would have preferred a more theoretical book focused within the author's wheelhouse without so many examples of applications because of this. More fundamentally, the scale-freeness of many networks is currently being challenged; I myself have observed deviations from this pattern in the networks I study. An update to Linked should include these challenges, and the limitations of the scale-free model in its ability to describe real networks.

  27. 4 out of 5

    Choudhry Shuaib

    Excellent book which really distils the concepts of networks and the current research happening in this area as well as a great recount of the history underlying the progress made in the field. Doesn't skip out on the science and explains it at a very accessible level and gives a good conceptual understanding of what the researchers are trying to achieve as well as the implications. Erdos and Renyi did incredible work on random networks but it doesn't fit in with the real life network structure Excellent book which really distils the concepts of networks and the current research happening in this area as well as a great recount of the history underlying the progress made in the field. Doesn't skip out on the science and explains it at a very accessible level and gives a good conceptual understanding of what the researchers are trying to achieve as well as the implications. Erdos and Renyi did incredible work on random networks but it doesn't fit in with the real life network structure where there isn't randomness in the links that form. The Watts-Strogratz model remedies some of this randomness by implementing a clustered world but this still doesn't account for hubs and connectors which are central parts of networks. The internet isn't as egalitarian as we think it is because of the structure of complex networks leads to hubs being increasingly likely. Directed networks break down into four continents like it does for the worldwide web. This structure inherently introduces bubbles which people inhabit and leads to fragmentation as well as ignorance and the development of echo chambers. Great chapter on the biological networks that make up the cells, metabolic reactions and the chemical reactions which underlie life. His chapter on the network economy is shockingly bad and full of assertions with no citations or evidence to back them up. Author seems to support the exclusionary small world nature of the elite or at the very least defends their laziness and nepotism. But I do agree with his main point that a more holistic perspective is needed in economics. The after link chapter on modules and the hierarchical nature of modules and networks in general is a great outlook for future research in networks. Overall a great introduction into networks.

  28. 5 out of 5

    YHC

    The author is a knowledge physicist who used a lot of examples from scientific, human relationship, biology, finance...etc aspects to explain the development of network. I thought i would not understand this book if i didn't read "Nine Algorithms That Changed the Future: The Ingenious Ideas That Drive Today's Computers", I got to know how google design its search engine, with what tricks some spams used (multiple links with tags from famous links, with famous words, sentences inserted in their p The author is a knowledge physicist who used a lot of examples from scientific, human relationship, biology, finance...etc aspects to explain the development of network. I thought i would not understand this book if i didn't read "Nine Algorithms That Changed the Future: The Ingenious Ideas That Drive Today's Computers", I got to know how google design its search engine, with what tricks some spams used (multiple links with tags from famous links, with famous words, sentences inserted in their pages in order to get the good position from the result of searching) . The author of this book used may very interesting attractive titles on many chapters that also related to the scientific stories that we can refer to what he wanted to explain the linked network. From Centralized, to branches then the best could actually be the "network" like a web. This web like network might look fragile but it was actually antifragile structure. means, each one collapses still could not harm the whole structures. (centralized and branches got this danger). It also mentioned "Six degrees of separation" and gave the example from "The tipping point" to explain some got more connection than others and play certain roles.

  29. 4 out of 5

    Laura

    This book wasn't what I expected, which meant I had to readjust my mindset going in. I had the impression it was about how disciplines and events are interlinked (more cause-and-effect), but is actually about the mathematics of modeling networks. Which turned out to be really interesting, if a little dry. I appreciated the history, and the gradual building up of our current understanding of networks; it seemed slow, but since I have no background in the subject, it was a good pace. He also const This book wasn't what I expected, which meant I had to readjust my mindset going in. I had the impression it was about how disciplines and events are interlinked (more cause-and-effect), but is actually about the mathematics of modeling networks. Which turned out to be really interesting, if a little dry. I appreciated the history, and the gradual building up of our current understanding of networks; it seemed slow, but since I have no background in the subject, it was a good pace. He also constantly reminds you of who people are and the differences between their models, which was helpful. I was disappointed by how much focus was placed on the Internet and World Wide Web (especially since that part shows its age quickly), but I suppose that is the author's area of research and is to be expected. I had hoped for more biological applications, rather than a single broad chapter. I was a little worn out by the end, so I would have appreciated the examples and applications being more dispersed throughout the book than collected in a somewhat jumbled mass at the end. Overall, a great introduction to network modeling, and some interesting food for thought.

  30. 4 out of 5

    Mer

    The book is eating on common fetish for platonist objects. Wouldn't that be more interesting to stir up debate and arguments circulating linkage? For example, the author introduced weak-strong link propositions, the survival of the fitness and mechanical image of networks. This seems like building a new hierarchy that is based on homogenous-diversity. First, Darwinism doesn't understand superfluous genes and their function, but maybe this ontology of function has narrowed our scope? It does not The book is eating on common fetish for platonist objects. Wouldn't that be more interesting to stir up debate and arguments circulating linkage? For example, the author introduced weak-strong link propositions, the survival of the fitness and mechanical image of networks. This seems like building a new hierarchy that is based on homogenous-diversity. First, Darwinism doesn't understand superfluous genes and their function, but maybe this ontology of function has narrowed our scope? It does not cover the lucky draw of mutation and how networks might only be identified in specific conditions. Although many forms are beyond observations, they should not be inferior to the foreseeable network. Second, linkage in Barabasi's sense seems to ignore a broader understanding of metabolism, reproduction, adaptation and thermodynamics. What might be interesting is to expand the structural description of links to acknowledging the limits of observation, as many are formless to naked eyes. Maybe shifting object studies to the network is a clumsy manner for understanding what moves? Or maybe we can expand our sense for how neuro-network consolidate ideas?

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