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Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life
How is the human brain like the AIDS epidemic? Ask physicist Albert-László Barabási and he'll explain them both in terms of networks of individual nodes connected via complex but understandable relationships. Linked: The New Science of Networks is his bright, accessible guide to the fundamentals underlying neurology, epidemiology, Internet traffic, and many other fields united by complexity.
Barabási's gift for concrete, nonmathematical explanations and penchant for eccentric humor would make the book thoroughly enjoyable even if the content weren't engaging. But the results of Barabási's research into the behavior of networks are deeply compelling. Not all networks are created equal, he says, and he shows how even fairly robust systems like the Internet could be crippled by taking out a few super-connected nodes, or hubs. His mathematical descriptions of this behavior are helping doctors, programmers, and security professionals design systems better suited to their needs. Linked presents the next step in complexity theory--from understanding chaos to practical applications. --Rob Lightner
305 pages, Kindle Edition
First published January 1, 2002
418 people are currently reading
8702 people want to read
About the author
Albert-László Barabási
18 books393 followersAlbert-László Barabási is a physicist, best known for his work in the research of network science. A Hungarian born native of Transylvania, he received his Masters in Theoretical Physics at the Eotvos University in Budapest, Hungary and was awarded a Ph.D. three years later at Boston University. Barabási is the author of six books, including the forthcoming book "The Formula: The Science of Success." His work lead to the discovery of scale-free networks in 1999, and proposed the Barabási-Albert model to explain their widespread emergence in natural, technological and social systems, from the cellular telephone to the WWW or online communities.
Barabási is both the Robert Gray Dodge Professor of Network Science and a Distinguished University Professor at Northeastern University, where he directs the Center for Complex Network Research, and holds appointments in the Departments of Physics and Computer Science, as well as in the Department of Medicine, Harvard Medical School and Brigham and Women Hospital, and is a member of the Center for Cancer Systems Biology at Dana Farber Cancer Institute.
Barabási is a Fellow of the American Physical Society. In 2005 he was awarded the FEBS Anniversary Prize for Systems Biology and in 2006 the John von Neumann Medal by the John von Neumann Computer Society from Hungary, for outstanding achievements in computer-related science and technology. In 2004 he was elected into the Hungarian Academy of Sciences and in 2007 into the Academia Europaea. He received the C&C Prize from the NEC C&C Foundation in 2008. In 2009 APS chose him Outstanding Referee and the US National Academies of Sciences awarded him the 2009 Cozzarelli Prize. In 2011 Barabsi was awarded the Lagrange Prize-CRT Foundation for his contributions to complex systems, awarded Doctor Honoris Causa from Universidad Politcnica de Madrid, became an elected Fellow in AAAS (Physics) and is an 2013 Fellow of the Massachusetts Academy of Sciences.
Barabási is both the Robert Gray Dodge Professor of Network Science and a Distinguished University Professor at Northeastern University, where he directs the Center for Complex Network Research, and holds appointments in the Departments of Physics and Computer Science, as well as in the Department of Medicine, Harvard Medical School and Brigham and Women Hospital, and is a member of the Center for Cancer Systems Biology at Dana Farber Cancer Institute.
Barabási is a Fellow of the American Physical Society. In 2005 he was awarded the FEBS Anniversary Prize for Systems Biology and in 2006 the John von Neumann Medal by the John von Neumann Computer Society from Hungary, for outstanding achievements in computer-related science and technology. In 2004 he was elected into the Hungarian Academy of Sciences and in 2007 into the Academia Europaea. He received the C&C Prize from the NEC C&C Foundation in 2008. In 2009 APS chose him Outstanding Referee and the US National Academies of Sciences awarded him the 2009 Cozzarelli Prize. In 2011 Barabsi was awarded the Lagrange Prize-CRT Foundation for his contributions to complex systems, awarded Doctor Honoris Causa from Universidad Politcnica de Madrid, became an elected Fellow in AAAS (Physics) and is an 2013 Fellow of the Massachusetts Academy of Sciences.
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April 17, 2009
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.
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.
September 14, 2011
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.
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.
September 22, 2015
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! : )
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! : )
November 23, 2012
O tipo de livro que coloco ao lado do The Information: A History, a Theory, a Flood do James Gleick na categoria "livros que fazem sua visão de mundo mudar". Recomendo o Linked para qualquer pessoa que trabalhe com ciência ou com relacionamentos humanos ou entre qualquer variável. A noção de como redes são construídas e funcionam, de sites a pessoas, ou mesmo proteínas, e como as propriedades que explicam este tema são relevantes para tudo que fazemos.
Embora seja de 2002 e esteja um pouco defasado, ainda é um livro bastante relevante, leitura fundamental para quem trabalha com redes complexas. Uma descrição de como esta área surgiu, como cresceu recentemente e o que está se tornando, em primeira mão por um dos autores mais importantes da área.
Embora seja de 2002 e esteja um pouco defasado, ainda é um livro bastante relevante, leitura fundamental para quem trabalha com redes complexas. Uma descrição de como esta área surgiu, como cresceu recentemente e o que está se tornando, em primeira mão por um dos autores mais importantes da área.
June 3, 2020
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.
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.
February 6, 2017
Awesome book if you want to know why should we think in networks
July 29, 2007
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!
April 10, 2014
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?)
January 12, 2015
This was incredible. I want to pursue this field full time, professionally after reading this book.
July 28, 2023
Even though is not my usual type of book, I found it very interesting and well-written
January 5, 2016
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...
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...
March 5, 2013
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.
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.
June 16, 2021
I really like this book. Nice popular science book to give intuitions of the field "Network Science". Network appears in a lot of places, including social network of friends, the web's five billion websites, the biological food chain, business and commerce, the growth of cities, intra-cellular proteins.
Why reading the book: Having a better understanding of networks can help us with lots of things such as understanding our neighborhood, company organization and dynamics, understanding our own career network. It can also help solve big social problems such as the spread of epidemics, fighting against terrorism, handling economic crises or solving social problems of the society.
Contents: how networks emerge, what they look like, and how they evolve
1) Random-distribution networks (Leonhard Euler): connection probability p=0, ..., 1/N, 1. The number neighbors for each node (i.e., node degree) follows Gaussian distribution.
2) However, the networks in the real-world are not randomly distributed. For social networks, in 1960s, Stanley Milgram finds six-degrees-of-separation, and well-connected people have three degrees of separation. Finding jobs is highly related to network clusters, Mark Granovetter finds that weak contacts help 28% people to find jobs, and strong contacts help 17%.
3) Internet: Node degrees are long-tailed, i.e., the sum of degrees of big nodes > 50%. A network can be dominated by a few very highly connected nodes. Just like airline connections makes most city-to-city flights with only 0-2 transits.
4) The node degrees follow exponential growth, and therefore the distribution of node degrees is logarithmic. (Maybe the reason for the exponential growth is that each node degree increase is a function of the original node degree.) Preferential linking means that each new node, it is more likely to link to big nodes.
5) Network visualization can help identify organization problems in companies, market surveys, etc., and it can provide interesting research insights into international politics, geodemographic clusters, etc.
6) Robust of network when some nodes disappear: Think of extinction of some plants and animals. It decreases biodiversity, but only when some important nodes disappears, the biosystem will have a collapse. Same applies to climate change, network failure, tumor in the brain's neural networks, etc.
More resources:
- Videos of data visualization, and talks by the author: http://primo.ai/index.php?title=Netwo...
- List of phenomena in natural and social science that also follows the power law: https://www.researchgate.net/figure/S...
Why reading the book: Having a better understanding of networks can help us with lots of things such as understanding our neighborhood, company organization and dynamics, understanding our own career network. It can also help solve big social problems such as the spread of epidemics, fighting against terrorism, handling economic crises or solving social problems of the society.
Contents: how networks emerge, what they look like, and how they evolve
1) Random-distribution networks (Leonhard Euler): connection probability p=0, ..., 1/N, 1. The number neighbors for each node (i.e., node degree) follows Gaussian distribution.
2) However, the networks in the real-world are not randomly distributed. For social networks, in 1960s, Stanley Milgram finds six-degrees-of-separation, and well-connected people have three degrees of separation. Finding jobs is highly related to network clusters, Mark Granovetter finds that weak contacts help 28% people to find jobs, and strong contacts help 17%.
3) Internet: Node degrees are long-tailed, i.e., the sum of degrees of big nodes > 50%. A network can be dominated by a few very highly connected nodes. Just like airline connections makes most city-to-city flights with only 0-2 transits.
4) The node degrees follow exponential growth, and therefore the distribution of node degrees is logarithmic. (Maybe the reason for the exponential growth is that each node degree increase is a function of the original node degree.) Preferential linking means that each new node, it is more likely to link to big nodes.
5) Network visualization can help identify organization problems in companies, market surveys, etc., and it can provide interesting research insights into international politics, geodemographic clusters, etc.
6) Robust of network when some nodes disappear: Think of extinction of some plants and animals. It decreases biodiversity, but only when some important nodes disappears, the biosystem will have a collapse. Same applies to climate change, network failure, tumor in the brain's neural networks, etc.
More resources:
- Videos of data visualization, and talks by the author: http://primo.ai/index.php?title=Netwo...
- List of phenomena in natural and social science that also follows the power law: https://www.researchgate.net/figure/S...
March 9, 2024
A good book by Barabási that covers networks applied to business, scientific collaboration, cellular biology, and early 21st-century computer science, all built on a foundation of physics and math.
In particular, I learned about the scale-free topology of networks, their power-law foundation, their relationship to complexity, a bit about the Erdős–Rényi random graph model, and eventually, the modularity of scale-free networks in living systems and organizations. Barabási provides many examples to explain the omnipresence of networks in our daily lives.
I also liked seeing the predictions for the future and the description of the state of the art of the WWW in 2002.
Going through the notes, I was blown away by how much the author knows. It's like he dove deep into so many topics and had all the latest info at his fingertips when he wrote the book. You can tell he did a ton of research to put it all together. He was all over stuff like personalized medicine, complexity, and computational biology – which aren’t just hot topics today; they're still pretty much the whole game now. Plus, he's got loads of papers out in top journals and is a pioneer in applying scale-free networks.
It's also wild how many predictions still haven't come true. It really shows we've got to roll up our sleeves and make tech and societal advancements happen ourselves – we can't just assume they'll happen on their own.
P.S.: I first started reading it back in 2018 when I was 18 years old but never finished it. Well, now the time has come, and I've finally done it! Haha
In particular, I learned about the scale-free topology of networks, their power-law foundation, their relationship to complexity, a bit about the Erdős–Rényi random graph model, and eventually, the modularity of scale-free networks in living systems and organizations. Barabási provides many examples to explain the omnipresence of networks in our daily lives.
I also liked seeing the predictions for the future and the description of the state of the art of the WWW in 2002.
Going through the notes, I was blown away by how much the author knows. It's like he dove deep into so many topics and had all the latest info at his fingertips when he wrote the book. You can tell he did a ton of research to put it all together. He was all over stuff like personalized medicine, complexity, and computational biology – which aren’t just hot topics today; they're still pretty much the whole game now. Plus, he's got loads of papers out in top journals and is a pioneer in applying scale-free networks.
It's also wild how many predictions still haven't come true. It really shows we've got to roll up our sleeves and make tech and societal advancements happen ourselves – we can't just assume they'll happen on their own.
P.S.: I first started reading it back in 2018 when I was 18 years old but never finished it. Well, now the time has come, and I've finally done it! Haha
November 4, 2021
Természetesen nem értettem mindent (gráfelmélet távol áll tőlem :D vagy én tőle, ki tudja?), és 2002-es a kötet eszméletlen sok minden megváltozott azóta, de mégis 5 csillag, mert nagyon igyekszik komplex témákat olvasmányosan tálalni. Egészen kedvet kap tőle az ember további ilyen művek olvasásához!
June 18, 2022
honestly skipped 100 pages in the middle because it was just too repetitive to keep my interest. each chapter alone is an interesting collection of studies, but all together, i couldn’t get myself to keep churning through
January 24, 2014
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.
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.
May 14, 2021
I read this book first time in 2003 (in Hungarian), and I was impressed as a teenager. I wanted to share the experience with everyone, and some of them might have been interested enough to borrow it. I've never seen my beautiful book again. Recently network science became relevant for me, and I was considering to reread this title for a start. Honestly, I don't like the newer editions, so I needed to search in a second-hand bookshop, for the original, harcover edition with the beautiful veined leaf.
Anyway, it is fun to read this book in 2021, but I can still highly recommend it, beacause it is easy to read and exciting at the same time.
Anyway, it is fun to read this book in 2021, but I can still highly recommend it, beacause it is easy to read and exciting at the same time.
July 8, 2025
Cartea Linked: The New Science of Networks scrisă de Albert-László Barabási este o incursiune fascinantă în lumea rețelelor, oferind cititorilor o privire de ansamblu asupra modului în care conexiunile invizibile structurează lumea – de la internet și rețele sociale, până la sistemele biologice și economice. Volumul este scris într-un stil accesibil, dar cu un pronunțat caracter tehnic, ceea ce poate surprinde pe cititorii care se așteaptă la o lectură pur popularizatoare. Barabási introduce concepte precum „noduri”, „legături”, „puterea legăturilor slabe” sau „nodurile hub”, explicându-le cu exemple sugestive, dar solicitând uneori o atenție sporită din partea celor fără pregătire științifică.
Totuși, una dintre limitele majore ale cărții este legată de momentul publicării: apărută în 2002, Linked reflectă o etapă incipientă în cercetarea rețelelor complexe. Multe dintre referințele și exemplele utilizate (precum Napster sau structura inițială a Web-ului) par astăzi depășite. În era rețelelor de inteligență artificială, blockchain sau a economiilor bazate pe date masive, unele explicații nu mai rezonează la fel de puternic.
Cu toate acestea, Linked rămâne valoroasă pentru că fundamentează terminologia și conceptele de bază ale teoriei rețelelor. Este o carte utilă pentru cei care vor să înțeleagă cum funcționează lumea interconectată, dar care trebuie abordată cu răbdare și un ochi critic asupra actualității informațiilor.
Totuși, una dintre limitele majore ale cărții este legată de momentul publicării: apărută în 2002, Linked reflectă o etapă incipientă în cercetarea rețelelor complexe. Multe dintre referințele și exemplele utilizate (precum Napster sau structura inițială a Web-ului) par astăzi depășite. În era rețelelor de inteligență artificială, blockchain sau a economiilor bazate pe date masive, unele explicații nu mai rezonează la fel de puternic.
Cu toate acestea, Linked rămâne valoroasă pentru că fundamentează terminologia și conceptele de bază ale teoriei rețelelor. Este o carte utilă pentru cei care vor să înțeleagă cum funcționează lumea interconectată, dar care trebuie abordată cu răbdare și un ochi critic asupra actualității informațiilor.
June 21, 2020
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.
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.
This entire review has been hidden because of spoilers.
December 26, 2020
A quick and easy-to-read introduction to the concepts and applications of network studies, if outdated (this came out in 2002). Barabasi's work is highly influential in the field, and reading this, it's easy to understand why. Some compelling ideas here.
At the same time, even this overview revealed some of the critical flaws in network thinking, especially when it came to apparent socioeconomic applications of its ideas. This was particularly evident in the Network Economy chapter, where many of the worst parts of the modern economy are dismissed as inevitable, as a result of networked self-organizing, rather than the deliberate configurations of power. It reveals the dangers of applying an interesting concept in a totalizing manner across our universe of knowledge. A useful read on the whole, but with several caveats.
(my life hack: read the general audience book by an academic first so you don't lose your head trying to read the academic stuff.)
At the same time, even this overview revealed some of the critical flaws in network thinking, especially when it came to apparent socioeconomic applications of its ideas. This was particularly evident in the Network Economy chapter, where many of the worst parts of the modern economy are dismissed as inevitable, as a result of networked self-organizing, rather than the deliberate configurations of power. It reveals the dangers of applying an interesting concept in a totalizing manner across our universe of knowledge. A useful read on the whole, but with several caveats.
(my life hack: read the general audience book by an academic first so you don't lose your head trying to read the academic stuff.)
January 19, 2025
This is a somewhat outdated, yet interesting insight into how networks come to play in virtually everything: from biology to politics and from chemistry to economics. What I am missing is a more concrete, critical question that I would expect to come after the conclusion: now what? Now that we know that the economy, society and much of our health is best to be explained through networks, which are then the most important aspects of life to focus on, to improve or to revolutionise? Clearly, that wasn’t really part of the book’s goal, but it would have made this aging book all the more relevant for the year 2025.
April 13, 2021
Offers a different way tp see the world. It was written almost 20 years ago, and since then technology has made use of Networks in various parts of our lives, so some concepts may not be so foreign, but despite that, the book is a very interesting world, to learn about how the idea of "Networks" began and how it influenced all walks of Science
September 26, 2018
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.
April 29, 2019
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.
May 18, 2025
Fizikte kendime yeni bi yol mu buldum, bakalım
August 2, 2019
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.
February 15, 2025
An interesting introduction to the science of networks. It does however disappoint on a couple of levels.
First it builds the basic foundation of how networks are structured (nodes with links, power laws, scale-free models) in brief segments with clear explanations from a number of specific examples such as the internet or the network of scientific academics. It feels like the detail peters out half way through however. The remainder of the book is a sequence of fairly brief descriptions of how these principles can be applied to a number of different networks (the internal operation of a cell, the workings of a market economy, the structure of a large firm). These are interesting, but quite brief and though insightful not perhaps always entirely convincing.
Which leads to the second point. There is no doubt that this is a scientists approach to a number of different complex examples, and therefore sometimes drifts over into the mathematical explanation of phenomena that have been researched and understood non-mathematically. The book doesn't enter into any dialogue with this non-mathematical research (for example on the workings of the capitalist economy). This is probably understandable - for reasons of space if nothing else. But it does leave the book feeling like it's missing something.
An interesting introduction for the generalist reader then, but with limitations.
15 February 2025: Not much to add on second reading. It remains interesting for the non-mathematician, but a a bit brief, broken into chunks, and with a fundamental disconnect when talking about non-mathematical subjects. A bit disappointing.
First it builds the basic foundation of how networks are structured (nodes with links, power laws, scale-free models) in brief segments with clear explanations from a number of specific examples such as the internet or the network of scientific academics. It feels like the detail peters out half way through however. The remainder of the book is a sequence of fairly brief descriptions of how these principles can be applied to a number of different networks (the internal operation of a cell, the workings of a market economy, the structure of a large firm). These are interesting, but quite brief and though insightful not perhaps always entirely convincing.
Which leads to the second point. There is no doubt that this is a scientists approach to a number of different complex examples, and therefore sometimes drifts over into the mathematical explanation of phenomena that have been researched and understood non-mathematically. The book doesn't enter into any dialogue with this non-mathematical research (for example on the workings of the capitalist economy). This is probably understandable - for reasons of space if nothing else. But it does leave the book feeling like it's missing something.
An interesting introduction for the generalist reader then, but with limitations.
15 February 2025: Not much to add on second reading. It remains interesting for the non-mathematician, but a a bit brief, broken into chunks, and with a fundamental disconnect when talking about non-mathematical subjects. A bit disappointing.
January 19, 2009
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.
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.
October 14, 2012
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!
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!
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