Networks are everywhere. You are reading this page over a network. Telephones, road and rail, airlines, electricity, family and friends, LANs and WANs they are all examples of networks. Networks help us connect to people, to places, to computers, to utilities, and even to memories hidden deep inside the ultimate network of them all, our brain.
Consider for a moment our social networks and how theyve changed in the past few years. For most of us, there are now hundreds of people in our address books, more than that we could have imagined or managed a decade ago. High-school buddies, classmates from college, ex-colleagues from previous jobs, or simply family and friends email, IM and cellphones connect us to them. Geography and distance have become irrelevant as technology helps us manage 10X the relationships.
Networks and connections are nothing new; they have existed since time immemorial. Its not networks that are the 10X force, but our understanding of how networks work which is the real tsunami. Bringing networks to life is what a series of recent books have done: Malcom Gladwells “The Tipping Point“, Steven Johnsons “Emergence“, David Weinbergers “Small Pieces Loosely Joined” and above all, Albert-Lazlo Barabasis “Linked“. Each addresses a similar set of issues, but from different angles. Gladwell looks at how epidemics spread, Johnson looks at how the whole can be much greater than the sum of the parts, Weinberger considers the Internet and Web, while Barabasi examines the underlying science of networks.
Barabasis book delves into the formation of networks. He talks of scale-free networks networks in which power-law degree distribution applies. The degree distribution of random networks follows a bell curve, with most nodes have the same number of average links, and nodes with a very large number of links dont exist. The highway network can be thought of as a random network (with cities thought of as nodes, and the connecting roads as links). On other hand, in scale-free networks, most nodes have only a few links, held together by a few highly connected hubs. The air traffic system is an example of a scale-free network.
Barabasi goes on to say that real networks are governed by two laws: growth, which means that new nodes are continuously added, and preferential attachment, which states that new nodes prefer to attach to the more connected nodes a rich-get-richer kind-of phenomenon. Taken together, these two laws govern the evolution of scale-free networks, and result in the emergence of a few highly connected hubs.
Barabasi also introduces the notion of fitness, the ability to make friends relative to everyone else in the neighbourhood, a quantitative measure of a nodes ability to stay ahead of the competition. Googles success is an indication of the fit-get-rich behaviour of networks. Microsoft is an example of winner-take-all, meaning that the fittest node grabs all the links (a state described as the Bose-Einstein condensate), a situation where the network develops a star topology.
Barabasi also applies the learnings to the world of business: In a network economy the hubs must get bigger as the network grows. To satisfy their hunger for links, nodes of the business web learn to swallow smaller nodes, a novel method unseen in other networks. As globalization pressures the nodes to grow bigger, mergers and acquisitions are a natural consequence of an expanding economy.
Where do we go from here? Answers Barabasi:
The answer is simple. We must remove the wrapping [that surrounds networks]. The goal before us is to understand complexity. To achieve that, we must move beyond structure and topology and start focusing on the dynamics that take place along the links. Networks are only the skeleton of complexity, the highways for the various processes that make the world humTo understand life we must start looking at the reaction dynamics along the links of the metabolic network. To understand the Internet, we must add traffic to its entangled linksWe have learned the laws of web cartography, allowing us to draw new maps whenever we are faced with new systems. Now we must follow these maps to complete the journey, fitting the pieces to one another, node by node, link by link, and capturing their dynamic interplay.
The connections are there, and have always been there. What Barabasi and his colleagues in the academic community are doing is throwing light to make these relationships visible. As we understand networks better, so will our knowledge of the complexities of the connected world become richer.
Next Week: Techs 10X Tsunamis (continued)