What do you think?
Rate this book
Very Short Introductions #335
Networks: A Very Short Introduction
From ecosystems to Facebook, from the Internet to the global financial market, some of the most important and familiar natural systems and social phenomena are based on a networked structure. It is impossible to understand the spread of an epidemic, a computer virus, large-scale blackouts, or massive extinctions without taking into account the network structure that underlies all these phenomena. In this Very Short Introduction, Guido Caldarelli and Michele Catanzaro discuss the nature and variety of networks, using everyday examples from society, technology, nature, and history to explain and understand the science of network theory. They show the ubiquitous role of networks; how networks self-organize; why the rich get richer; and how networks can spontaneously collapse. They conclude by highlighting how the findings of complex network theory have very wide and important applications in genetics, ecology, communications, economics, and sociology.
136 pages, Paperback
First published October 25, 2012
61 people are currently reading
656 people want to read
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 - 23 of 23 reviews
February 8, 2013
The rise of Internet has put the idea of networks in the forefront of public consciousness, which has only been accentuated with the arrival of (online) social networks. However, explicit or implicit networks are a very salient part of our lives and have been so for quite some time: roads and railroads, kinship networks, commercial networks, are all just some of the examples of networks that we come across all the time. And then there are predator-pray networks, protein interaction networks, and a myriad other examples of networking phenomena. Once you adapt the network paradigm as a guiding principle of organizing the world you start seeing networks everywhere.
This is a very short introduction to networks which covers both the concrete examples of networks as well as the their theoretical description. There are many interesting historical vignettes in it, and quite a few conceptual insights. It is a very well written and resourced book. It has all the qualities that one has come to associate with these short introductions – written by an expert in the field, and yet accessible to a wide range of readers. I would highly recommend it to anyone with an interest in networks of any sort.
This is a very short introduction to networks which covers both the concrete examples of networks as well as the their theoretical description. There are many interesting historical vignettes in it, and quite a few conceptual insights. It is a very well written and resourced book. It has all the qualities that one has come to associate with these short introductions – written by an expert in the field, and yet accessible to a wide range of readers. I would highly recommend it to anyone with an interest in networks of any sort.
February 28, 2023
Solid introduction to network theory, going into the historical aspects, theoretical background and lots of real-world examples and applications.
October 9, 2023
Good introduction that provides an overview of the concept of networks and how it’s used in several different sciences.
November 10, 2014
This book gives a great case-based understanding of what networks are all about. There is minimal theory, however, as it is mostly a promotion for the fledgling science of networks. Mostly it gives the backing history of the mathematical languages used and the most prominent applications of the science as it is interpreted today.
A side note, this book is talking about a very new and growing field. Thus, within a few short years, this book may no longer be necessary for the bookshelves of those truly interested. Also most of the information contained can be looked up online fairly easily for free. However for $10, this book might be worth your time. For those with strong interest, and more mathematical background, I would suggest picking up Networks: An Introduction, also published by Oxford Press.
A side note, this book is talking about a very new and growing field. Thus, within a few short years, this book may no longer be necessary for the bookshelves of those truly interested. Also most of the information contained can be looked up online fairly easily for free. However for $10, this book might be worth your time. For those with strong interest, and more mathematical background, I would suggest picking up Networks: An Introduction, also published by Oxford Press.
December 23, 2018
Very simplistic, a lot of repetition, etc.
September 7, 2020
This would be a much better way to start learning about complexity science than the Very Short Introduction dedicated to it (Complexity: A Very Short Introduction, by John H. Holland).
If you're after a technical challenge, skip this Very Short Introduction. But if you want 114 pages you can fit in your pocket, a kind of Network Science for Dummies that won't talk down to you, this has your name on it.
I'll try to talk about what's in the book with a minimum of rambling, but that's probably a lost cause from the start.
Anyway, have you ever thought about why groups polarize and how that can be analyzed, predicted, or even prevented? Seriously, we're going to see a lot more of this in the future, and we can start thanking the science of networks as we make more use of it.
Or how about the commonality between a virus, loneliness, the giggles, life skills, and obesity? All spread through, and can reveal, networks and their underlying properties. Without even knowing, you're strongly influenced by the friends of people you know, and their friends.
What about this mysterious "power law" that keeps showing up everywhere? For example, across many languages, the most common word is about twice as common as the runner-up, about three times as common as the the word in third place, and so on, and so on. Why this pattern? It's odd, but then city sizes are the same way, and corporate balance sheets, and ecosystems, and that's kind of wild. Why does that happen? Is the world one big coincidence? Ok, the book doesn't fully explain, because that isn't fully understood to begin with, but you'll quickly appreciate the outline of what's going on. Unpacking the pattern into nodes and links makes it surprisingly simple (and this does seem to be mirrored in many real-world examples). The power law and what causes it is an extremely powerful principle, pun possibly intended.
Ok, then, so why is a complex system complex? Again, this is one of those $64-million questions, but the book gives a rough appreciation you might not have had before. It helped me see how networks defined by trends of interactions spontaneously organize into hierarchies that build things, and what that has to do with power laws (the most popular word that's twice as popular as the next most popular one, etc), network hubs, the scale-free property (about to get to that), etc. The book doesn't make a big deal out of it, but it uses the latest definition of complexity (without saying it's defining anything, it discusses the idea). A system is complex when the nodes of its networks influence the links, which in turn influence the nodes, which influence the links, etc.
Let's use an example from the book. The subway. Stations on the subway map (nodes) can have different numbers of people in them (different node states). The lines (links) between the stations are fixed on any given day, and do not rearrange according to the numbers of people in the stations. However, the numbers of people in the stations do respond to any change in the links that might occur (a train breaks down, blocking a line, and traffic rearranges with that in mind). This scenario itself is not complex, because while the links (lines) affect the nodes (traffic in the stations), the opposite is not really true. Take a wider view though, say over several years, and the system blossoms into complexity, because traffic patterns will inform the addition of new lines, and this affects traffic, which affects the map, which affects the traffic, etc. It's like the guitar's feedback with the mic, only it's between nodes and links in a net.
Hm, can you explain the difference between scale-free networks and the small-world effect? Scale-free nets are ones that are richly diverse in how many connections people (or nodes) have. So for example, on social media, many people have a few connections while a few (the hubs) have many, and there's everything in between, and no clear maximum. You know what a scale-free net looks and feels like because they're everywhere, but you may not have a name for them or know how they form. Similarly, the small-world effect is the famous "six degrees of Kevin Bacon" effect—everyone seems to know someone, or knows someone who knows someone, who X. And so we love to say "It's a small world!" But scale-free (unpredictably large hubs) and small-world (you could get in touch with anyone alive) are quite different. Not opposites, but not the same. Under what conditions do networks get these features? It's a pair of concepts I'd been introduced to a few times and found interesting, but I would not have been able to answer these questions, or I would have said things that were wrong. If this book did nothing else, it helped me out here.
If you're thinking about a network representation of some real system, or just looking at a pretty graph, what are the nodes, and what are the links? Are nodes people, and (say) a virus can flow along links (social contacts, or maybe the grid-like "network" of spatial proximity)? Or are the nodes cities, and people carrying the virus can flow along links between cities, which are either at outbreak levels of infection or not (different node states, pretty similar to a person being either sick or not)? Or are viruses nodes in their evolutionary tree? When you get new arrivals, are the new arrivals new links? New nodes? Both? How are new connections chosen? How do you understand a network of hundreds, thousands, millions, billions of entities? Can a node be in different "moods" and behave differently from moment to moment, or are we going to take thousands of people in the same social class and bundle them into one impersonal node? These are a few of your considerations when creating a network to depict a real system, or building one to simulate it.
Some will find it all obvious. The book introduces basic terms and concepts, including clusters of synonyms, analogies, and closely related ideas, and discusses real applications. It's been a while, so this refreshed my lexicon and added to it. (I am trying to avoid jargon in this review... you're welcome! Or... sorry if I failed!) The examples and graphs are illustrative, and while I'd heard about two-thirds of these stories and studies before, I found I was connecting them in new ways.
I'm quite impressed that the two authors are Italian. You'd never know! There are maybe four sentences I'm not entirely sure I grasped (ie, which I thought might, or then again might not, be vague or even inaccurate), and a few others I puzzled over until they clicked. The way they introduce "epidemic threshold" for viruses could be improved, for example. And I didn't think I agreed with their usage of "trophic species" (in the discussion of ecosystem food webs). I would have called that "guilds," but I looked it up, and they're right.
Still, you can expect a fly-by approach. You get just enough to start trying out these phrases yourself, no more. But what I love about it is how well it brings the odds and ends together and breathes life into them. It isn't just a Frankenstein, ie, a glorified glossary. Understanding is about connecting, and this is a book about connections. It gave me a sense of wonder. I think it could take someone from zero to conversant.
If any of my quibbles come from the text itself rather than me, I'll chalk it up to an impressively absent language barrier: you really won't detect any non-nativeness to the English, but maybe it's why a few sentences weren't ideal pedagogically. For comparison, there were at least twice as many sentences like that in Complexity: A Very Short Introduction, written by a native English speaker who can hardly be called a bad writer or communicator (though that book has gotten criticism).
When I was learning about complexity at school, I never got to the network analysis course (hence this choice). But I've worked with networks/graphs a bit since—spent a whole summer poring over network visualization papers and techniques, and another summer coding a network visualizing tool. It was paid work, so I guess I'm not a complete beginner. But for people at a variety of levels, this is a nice little book! If you're a scientist and you know something about networks, then a lot will be ho-hum, of course. But it could fill gaps, or just get you wondering. And if you know little to nothing about networks (not IT networks, but the general idea of networks throughout society and nature), then I highly recommend this book.
If you're after a technical challenge, skip this Very Short Introduction. But if you want 114 pages you can fit in your pocket, a kind of Network Science for Dummies that won't talk down to you, this has your name on it.
I'll try to talk about what's in the book with a minimum of rambling, but that's probably a lost cause from the start.
Anyway, have you ever thought about why groups polarize and how that can be analyzed, predicted, or even prevented? Seriously, we're going to see a lot more of this in the future, and we can start thanking the science of networks as we make more use of it.
Or how about the commonality between a virus, loneliness, the giggles, life skills, and obesity? All spread through, and can reveal, networks and their underlying properties. Without even knowing, you're strongly influenced by the friends of people you know, and their friends.
What about this mysterious "power law" that keeps showing up everywhere? For example, across many languages, the most common word is about twice as common as the runner-up, about three times as common as the the word in third place, and so on, and so on. Why this pattern? It's odd, but then city sizes are the same way, and corporate balance sheets, and ecosystems, and that's kind of wild. Why does that happen? Is the world one big coincidence? Ok, the book doesn't fully explain, because that isn't fully understood to begin with, but you'll quickly appreciate the outline of what's going on. Unpacking the pattern into nodes and links makes it surprisingly simple (and this does seem to be mirrored in many real-world examples). The power law and what causes it is an extremely powerful principle, pun possibly intended.
Ok, then, so why is a complex system complex? Again, this is one of those $64-million questions, but the book gives a rough appreciation you might not have had before. It helped me see how networks defined by trends of interactions spontaneously organize into hierarchies that build things, and what that has to do with power laws (the most popular word that's twice as popular as the next most popular one, etc), network hubs, the scale-free property (about to get to that), etc. The book doesn't make a big deal out of it, but it uses the latest definition of complexity (without saying it's defining anything, it discusses the idea). A system is complex when the nodes of its networks influence the links, which in turn influence the nodes, which influence the links, etc.
Let's use an example from the book. The subway. Stations on the subway map (nodes) can have different numbers of people in them (different node states). The lines (links) between the stations are fixed on any given day, and do not rearrange according to the numbers of people in the stations. However, the numbers of people in the stations do respond to any change in the links that might occur (a train breaks down, blocking a line, and traffic rearranges with that in mind). This scenario itself is not complex, because while the links (lines) affect the nodes (traffic in the stations), the opposite is not really true. Take a wider view though, say over several years, and the system blossoms into complexity, because traffic patterns will inform the addition of new lines, and this affects traffic, which affects the map, which affects the traffic, etc. It's like the guitar's feedback with the mic, only it's between nodes and links in a net.
Hm, can you explain the difference between scale-free networks and the small-world effect? Scale-free nets are ones that are richly diverse in how many connections people (or nodes) have. So for example, on social media, many people have a few connections while a few (the hubs) have many, and there's everything in between, and no clear maximum. You know what a scale-free net looks and feels like because they're everywhere, but you may not have a name for them or know how they form. Similarly, the small-world effect is the famous "six degrees of Kevin Bacon" effect—everyone seems to know someone, or knows someone who knows someone, who X. And so we love to say "It's a small world!" But scale-free (unpredictably large hubs) and small-world (you could get in touch with anyone alive) are quite different. Not opposites, but not the same. Under what conditions do networks get these features? It's a pair of concepts I'd been introduced to a few times and found interesting, but I would not have been able to answer these questions, or I would have said things that were wrong. If this book did nothing else, it helped me out here.
If you're thinking about a network representation of some real system, or just looking at a pretty graph, what are the nodes, and what are the links? Are nodes people, and (say) a virus can flow along links (social contacts, or maybe the grid-like "network" of spatial proximity)? Or are the nodes cities, and people carrying the virus can flow along links between cities, which are either at outbreak levels of infection or not (different node states, pretty similar to a person being either sick or not)? Or are viruses nodes in their evolutionary tree? When you get new arrivals, are the new arrivals new links? New nodes? Both? How are new connections chosen? How do you understand a network of hundreds, thousands, millions, billions of entities? Can a node be in different "moods" and behave differently from moment to moment, or are we going to take thousands of people in the same social class and bundle them into one impersonal node? These are a few of your considerations when creating a network to depict a real system, or building one to simulate it.
Some will find it all obvious. The book introduces basic terms and concepts, including clusters of synonyms, analogies, and closely related ideas, and discusses real applications. It's been a while, so this refreshed my lexicon and added to it. (I am trying to avoid jargon in this review... you're welcome! Or... sorry if I failed!) The examples and graphs are illustrative, and while I'd heard about two-thirds of these stories and studies before, I found I was connecting them in new ways.
I'm quite impressed that the two authors are Italian. You'd never know! There are maybe four sentences I'm not entirely sure I grasped (ie, which I thought might, or then again might not, be vague or even inaccurate), and a few others I puzzled over until they clicked. The way they introduce "epidemic threshold" for viruses could be improved, for example. And I didn't think I agreed with their usage of "trophic species" (in the discussion of ecosystem food webs). I would have called that "guilds," but I looked it up, and they're right.
Still, you can expect a fly-by approach. You get just enough to start trying out these phrases yourself, no more. But what I love about it is how well it brings the odds and ends together and breathes life into them. It isn't just a Frankenstein, ie, a glorified glossary. Understanding is about connecting, and this is a book about connections. It gave me a sense of wonder. I think it could take someone from zero to conversant.
If any of my quibbles come from the text itself rather than me, I'll chalk it up to an impressively absent language barrier: you really won't detect any non-nativeness to the English, but maybe it's why a few sentences weren't ideal pedagogically. For comparison, there were at least twice as many sentences like that in Complexity: A Very Short Introduction, written by a native English speaker who can hardly be called a bad writer or communicator (though that book has gotten criticism).
When I was learning about complexity at school, I never got to the network analysis course (hence this choice). But I've worked with networks/graphs a bit since—spent a whole summer poring over network visualization papers and techniques, and another summer coding a network visualizing tool. It was paid work, so I guess I'm not a complete beginner. But for people at a variety of levels, this is a nice little book! If you're a scientist and you know something about networks, then a lot will be ho-hum, of course. But it could fill gaps, or just get you wondering. And if you know little to nothing about networks (not IT networks, but the general idea of networks throughout society and nature), then I highly recommend this book.
February 17, 2016
Não é a melhor das introduções do tema que tive a oportunidade de ler.
Apesar disso, é um texto curto que introduz o essencial sobre a concepção do mundo organizado em torno do conceito de rede e não de sistema, em 115 páginas.
Algo genérico e baseado nos elementos mais clássicos do tema, não acrescente nem corre grandes riscos. Útil, essencialmente, para uma primeira leitura sobre o tema.
Esta coleção possui alguns títulos interessantes e constitui uma introdução rápida sobre temas, com atualidade e rigor, escritos por especialistas e editados de forma capaz. São livros de bolso, bons para levar em viagem e apenas lamento que o tamanho de letra exija algum esforço para realizar a sua leitura...
Apesar disso, é um texto curto que introduz o essencial sobre a concepção do mundo organizado em torno do conceito de rede e não de sistema, em 115 páginas.
Algo genérico e baseado nos elementos mais clássicos do tema, não acrescente nem corre grandes riscos. Útil, essencialmente, para uma primeira leitura sobre o tema.
Esta coleção possui alguns títulos interessantes e constitui uma introdução rápida sobre temas, com atualidade e rigor, escritos por especialistas e editados de forma capaz. São livros de bolso, bons para levar em viagem e apenas lamento que o tamanho de letra exija algum esforço para realizar a sua leitura...
May 27, 2015
Not sure what I expected from this book but it was not interesting--lots of random textbook like disconnected information, lots of vocabulary describing what a network would look like in a graph or picture instead of just having an actual or picture (this book could be massively improved just by providing more illustrations) AND I learned a lot more about networks from Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives AND Connected was far more interesting to read.
July 1, 2015
I've been consistently surprised by the Very Short Introduction series, and this book was no exception. As usual, the first half is a very basic summary of things widely discussed by practically every other author. But starting in chapter 6 this book dived into topics rarely breached. It's both inspiring and educating.
December 31, 2014
Overall, an interesting introduction to the topic. Seemed to wander a bit in the middle.
December 28, 2021
A fantastic introduction and stepping stone to more thorough books
October 15, 2021
To begin with, I must admit that it is a real page-turner. Honestly, I didn't read many pop-science books that would be so engaging and non-exhausting to read. My choice fell on this book because of my research interest. Currently, I am a Master's student in international economics and my field of research is global production networks. Working with papers on this subject I felt the sense of incompletion. I mean, I didn't feel as if existing literature satisfied my own interest in research into what exactly is a production network and which universal properties does it have with respect to the network structure in general. So I decided to study the subject of networks from scratch in order to get a grasp on the network as a phenomenon and to know a bit more about some general approaches to its analysis and measurement.
Regarding this book, in particular, there's no need in saying that this is just a beautiful book for everyone who shares an interest in network analysis. First, it's a very carefully structured book that doesn't contain any complex material available only for the research community. Secondly, it is not limited to some particular field (for instance, besides some reference to economic networks, I also learned pretty much about social networks as one of the most studied types of real-world networks). Thirdly, it is definitely a mind-shaping book. What I mean by it is that while reading this book, you subconsciously acquire a new perspective - a network one. You begin to perceive the world around you in terms of network structures formatted of nodes and edges. For example, once you look at the subway scheme, you will unintentionally think about how many nodes does it comprise, what is the shortest path between the two of them, is this network resilient, and some other stuff like that which I'm not going to disclose here before you read the book itself.
Truly, a network approach is now getting exceptionally relevant, as long as the current pandemic also follows some general patterns of a network life circle. Cyber-security issues as well can't be addressed without taking account of the network structure properties of the WWW.
Another important thing about the book is that the author has prudently included a list of must-reads in the network field for anyone who will be inspired enough to look deeper into the problem of network science.
Considering my own research issue, I can say that this book has equipped me with a fine number of tools and insights for production network analysis. Namely, now I am more aspired to study topological characteristics of networks in the world economy.
To summarize, this book is definitely worth reading. Despite much of the terminology that I might've used above, this book is designed in the way that all of the necessary terms are introduced to the reader step by step, thus creating a solid understanding of the concepts.
Regarding this book, in particular, there's no need in saying that this is just a beautiful book for everyone who shares an interest in network analysis. First, it's a very carefully structured book that doesn't contain any complex material available only for the research community. Secondly, it is not limited to some particular field (for instance, besides some reference to economic networks, I also learned pretty much about social networks as one of the most studied types of real-world networks). Thirdly, it is definitely a mind-shaping book. What I mean by it is that while reading this book, you subconsciously acquire a new perspective - a network one. You begin to perceive the world around you in terms of network structures formatted of nodes and edges. For example, once you look at the subway scheme, you will unintentionally think about how many nodes does it comprise, what is the shortest path between the two of them, is this network resilient, and some other stuff like that which I'm not going to disclose here before you read the book itself.
Truly, a network approach is now getting exceptionally relevant, as long as the current pandemic also follows some general patterns of a network life circle. Cyber-security issues as well can't be addressed without taking account of the network structure properties of the WWW.
Another important thing about the book is that the author has prudently included a list of must-reads in the network field for anyone who will be inspired enough to look deeper into the problem of network science.
Considering my own research issue, I can say that this book has equipped me with a fine number of tools and insights for production network analysis. Namely, now I am more aspired to study topological characteristics of networks in the world economy.
To summarize, this book is definitely worth reading. Despite much of the terminology that I might've used above, this book is designed in the way that all of the necessary terms are introduced to the reader step by step, thus creating a solid understanding of the concepts.
December 2, 2021
I like the VSI series, it sort of feels like a 101 course in the name sake of the book. It avoids mathematical formulas, it is easy to pick up and read one chapter which typically reminds me of university lectures.
This book on network theory is very general but it amazed me how recently weighted graphs or rule based generation were introduced. Overall the final chapter puts a warning on the amazing effects which were alluded to throughout the book. Then general behaviours and priciples were still interesting to understand and it is a very good introduction to the terminology used.
Compared to other VSI books (that I have read) I would put this one on the mid-lower end of the spectrum. I still have a few more at home to read but this review is temporal and I can only compare against the books I read in the past.
In terms of editing there was only one mistake that I found. Figure 1, has the wrong caption desciption describing the arrows the predator prey relationship is reversed.
Next VSI is Game Theory....
This book on network theory is very general but it amazed me how recently weighted graphs or rule based generation were introduced. Overall the final chapter puts a warning on the amazing effects which were alluded to throughout the book. Then general behaviours and priciples were still interesting to understand and it is a very good introduction to the terminology used.
Compared to other VSI books (that I have read) I would put this one on the mid-lower end of the spectrum. I still have a few more at home to read but this review is temporal and I can only compare against the books I read in the past.
In terms of editing there was only one mistake that I found. Figure 1, has the wrong caption desciption describing the arrows the predator prey relationship is reversed.
Next VSI is Game Theory....
June 12, 2017
The book is a short (137 pages) and easy introduction to Networks. It provides a good number of interesting examples of various networks in the world, it mentions quite a few properties and metrics of networks, and it briefly explains how various properties are related and how they are related to various observed phenomena in networks. It does all of this without using mathematical definitions or formula. So, the book is really accessible.
If networks pique your interest, then start with this book.
If networks pique your interest, then start with this book.
July 17, 2025
This book offers a bite-size primer on network science. It balances technical depth with plain language. I never felt lost, though the pacing isn’t thrilling. Still, it delivers on its promise: a survey of the field. I appreciated that it goes beyond broad overviews, exploring sub-areas like social graphs and communication networks. By the end, I could tell if diving deeper into networks was my thing.
March 21, 2021
Muy parecido a "Linked" de A. Barabási, incluso contiene muchos de los mismos ejemplos usados ahí. De todas maneras, útil para saber lo básico sobre Redes Complejas y bastante más corto que "Linked".
December 5, 2021
While a bit more academic in nature for casual reading, the book does a Nice job of bringing real world situations under the network microscope, and provides a valuable look at how understanding the nuances of networks can help us better understand the modern world.
May 13, 2025
I loved this book. They are great at giving examples. It is approaching 15 years since those examples were most relevant, and the new algorithmic age requires an update. I would love to read the detective-style historical fiction version of this book.
February 6, 2022
A very sort concise and clear presentation of networks and why they are important. The series is great and deserves to be part of your non-fiction reading.
Read
October 2, 2024En algunas cosas suena redundante, pero se entiende porque, como su título lo dice, es una pequeña introducción.
April 16, 2016
This is a wide and loose discussion about how networks can unveil hidden complexity in systems. The whole thing is illustrated meshing up networks such as social interactions and web links. No metric/concept is defined formally, though.
December 11, 2017
I find that it delivered exactly what it promised: A very short introduction. Of course not more but not less either. It was comfortably written and I enjoyed within no time, a day or two or so.
April 18, 2017
Weak on the biology, but insightful in other areas.
Displaying 1 - 23 of 23 reviews