The first major concept in chapter ten is innovation and innovators. These are the people, usually a small group, willing to take risks with brand new ideas and products (127). We then learn that innovations spread from innovators to hubs, which then send the information out along their numerous links.
Barabasi defines hubs, in the context of social media and human networks, as the statistically rare, highly connected individuals who keep social networks together (129). He restates the definition of hubs by writing that often referred to as “opinion leaders”, “power users”, or “influencers” in marketing terms, human hubs are people who communicate with more people about a certain product than the average person (129). Because of their numerous contacts they are among the first to notice and use the experience of innovators (130).
In the second major concept of chapter ten, Barabasi next asks why some inventions, rumors, and viruses take over the globe, while others spread only partially or disappear (131). This is where the reader is introduced to the threshold model, where a threshold is assigned to each individual quantifying the likelihood that s/he will adopt an innovation (131).
We then learn about the spreading rate, which is the likelihood that an innovation will be adopted by a person introduced to it. But, the spreading rate by itself is not enough to decide the fate of an innovation, Barabasi tells us. For that, the critical threshold has to be known.
Critical threshold – a quantity determined by the properties of the network in which the innovation spreads. If the spreading rate of the innovation is less than the critical threshold, it will die out shortly. If it is over the threshold, the number of people adopting it will increase until everybody who could use it does (131).
Barabasi writes that for decades nobody questioned the spreading rate and critical threshold paradigm, but that “recently” (circa 2002) it had been learned that some viruses and innovations were oblivious to it. This is because in scale-free networks the critical threshold disappeared, making viruses traveling on them “practically unstoppable”, due to the uneven topology of the Internet (135). An infected hub will pass the virus to all the other computers linked to it, and in scale-free networks those infected computers have a good chance of surviving the virus while still passing it on. Barabasi writes that these results are not limited to computer viruses (135).
We move on to learn about Paul Baran who, in 1964 while employed at RAND Corporation, suggested three possible architectures for the Internet – centralized, decentralized, and distributed – when tasked to develop a communication system that would survive a nuclear attack:
- Centralized – Topology is star-like; along with the decentralized network dominated the structure of communications systems of the time.
- Decentralized – Topology is sets of stars connected to form one large star; along with the centralized network dominated the structure or communications systems of the time.
- Distributed – Topology is mesh-like; redundant so if some nodes fail other paths maintain the connection between the remaining nodes.
According to Baran, the only topology that could survive a nuclear attack was the distributed model. However, Barabasi states that Baran’s distributed network could only have worked and become a reality “if the Internet had continued to be regulated and maintained by the military.” (147)
Next, we are introduced to Internet mapping and given brief introductions to a couple of men and an organization who pioneered the field. Bill Cheswick and Hal Burch produced a map called the millennium map depicting the Internet’s topology on January 1, 2000. The Cooperative Association for Internet Data (CAIDA), monitors everything about the Internet, from “traffic to topology”.
Barabasi writes that there are “important practical reasons” for needing a global Internet map. He asserts that without knowing the Internet’s topology, it is not possible to design better tools and services to use it. He also asserts that the people who designed the Internet’s basic structure, still in place today, never imagined today’s uses of it, such as email or the World Wide Web (149).
According to Barabasi, the World Wide Web is an example of a success disaster and claims had its original creators foreseen how it would be used they would have designed a different infrastructure, resulting in a smoother experience (149).
Success disaster – When the design of a new function escapes into the real world and multiplies at an unseen rate before the design is fully in place.
We go on to learn that even though it was designed by humans, the Internet has “all the characteristics of a complex evolving system, making it more similar to a cell than a computer chip.” (149)
Chapter twelve opens by telling us of the bold claims made by Internet search engines in the early days of the Web. The leading search engines of the day boasted that they covered the entire Web. But, a research paper, titled Searching the World Wide Web (PDF), published in the journal Science in April 1998 “undermined” the search engines’ claims (162).
Lee Giles and Steve Lawrence built a meta search robot that visited search engines and asked them to fetch documents containing the word “crystal”. HotBot returned the largest number of documents, but they only covered 34 percent of the Web. AltaVista turned out to cover only 28 percent of the Web, and Lycos a mere 2 percent (163). Lawrence and Giles’ study awakened the public to the embellished claims of the search engines, but it also revealed that in 1999 there was huge swaths of the Internet that wasn’t seen – a full 60 percent (164). However, Barabasi writes that the topology of the Web limits our ability to see everything on it, anyway, because the Web is made up of four continents.
The first continent is called the central core. It contains about 25 percent of all Webpages and is home to all major sites. It’s easy to navigate because there is a path between any two documents housed on it (167). The second continent is called IN. It is a large as the central core but not as easy to navigate. The central core can be reached from it, but there is no way back to IN from there.
The third continent is called OUT. It, too, is as large as the central core but, like IN, is harder to navigate. It can be reached from outside with no problem, but once you get in you cannot get out. The fourth continent is comprised of tendrils and disconnected islands. They are isolated groups of pages, linked to each other, that cannot be reached from the central core and do not have links coming back to it. This continent has about 25 percent of all Web documents as well, and some of the isolated groups can contain thousands of Web docs (168).
The major concept of chapter thirteen is the network of the human cell. Barabasi teaches us that there are three network types – all scale-free, of course – in a human cell and two gene functions (182-183).
Three Types of Human Cell Networks:
- Metabolic – A web of hundreds of multistep intracellular biochemical reactions. Nodes can be simple chemicals or more complex molecules made of dozens of atoms. Links are the biochemical reactions that take place between these molecules.
- Regulatory – Controls everything within a cell, from metabolism to cell death. Nodes are the genes and proteins encoded by the DNA molecule. Links are the biochemical reactions between these components.
- Cellular – A sum of all cellular components such as genes, proteins, and other molecules connected by all physiologically relevant interactions, ranging from biochemical reactions to physical links. Contains all metabolic, protein-protein, and protein-DNA interactions present in the cell.
Barabasi explains, on page 183, that genes play two roles in the cellular network: structural and functional. In their structural role, they determine the scope and make of proteins and pass that information on to future generations (heredity). Genes’ structural role can be “unearthed” from its sequence. The functional role of genes, according to Barabasi, is apparent only in the dynamic context in which a gene interacts with many other components of a cell.
In chapter fourteen, we the most important thing we learn is that the networks behind all twentieth century corporations have the same structure; a tree. Barabasi writes that at the root is the CEO and the branches are the lower level managers and workers, and despite its pervasiveness there are many problems with the corporate tree (201). First, he says, information must be carefully filtered as it rises in the hierarchy, because if not filtered well when it reaches the top of the tree the overload could be huge.
Secondly, he maintains that integration leads to unexpected organizational rigidity and uses Ford’s car factories as a practical example, as it was one of the first manufacturing plants to fully implement the hierarchical organization. According to the author, Ford’s assembly lines became so tightly integrated and optimized that even small changes in the design of a car required shutting down the factory for weeks or months. Optimization, Barabasi writes, leads to Byzantine monoliths, which are organizations that are so over-organized that they are inflexible and unable to respond to changes in the business environment (201).
Barabasi sums up the tree model as being best suited for mass production, which he says was the way of economic success up until recently (2002), but that now economic value was found in ideas and information, a paradigm change dubbed the information economy (201). He then tells us that the most visible element of this remaking is a shift from a tree to a web or network organization, flat and with lots of cross-links between the nodes; and that companies wanting to compete in a fast-moving marketplace are shifting from a static and optimized tree into a dynamic and evolving web, offering a more flexible command structure (202).
Barabasi ends Linked in chapter fifteen by telling us one important thing: network thinking is poised to invade all domains of human activity and most fields of human inquiry. He tells us that it is not just a helpful point of view or tool, but that networks are by their very nature the fabric of most complex systems, and that nodes and links deeply infuse all strategies aimed at approaching our interlocked universe (222).