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Introduction  |  More is Different: Why "Social Software," Why Now?  |  Social Network Analysis: The Big Deal About Small Worlds  |  The Big Picture: Friendster, Ryze and Meetup  |  Case Study Analysis: Friendster, Ryze and Meetup  |  Directed Searching: Working the Network  |  Conclusions  |  Sources  |  Printer-friendly Version
Social Network Analysis: The Big Deal About Small Worlds

In his 2000 book The Social Life of Information, John Seely Brown intones: " celebrating access to information, pundits may undervalue the power of technology to create and deploy social networks."[25] When the World Wide Web first hit it big, and the dotcom bubble started to inflate, Brown's admonition was fitting. Social software innovation had stagnated. Programmers were lured off to preposterously high-paying industry jobs. Users grew accustomed to online interactions mediated by corporations that cast them as consumers. As dotcom disillusionment slowly drifted in, however, people began rediscovering the reason they had flocked to the network in the first place -- each other. Slowly, social software implementation started to pick up again in a bottom-up disorganized fashion. People simply started building the applications that they wanted to use themselves. When social network analysis went mainstream, it opened up new ways of thinking about these online communities. Social network analysis maps so well to the characteristics of social software, in fact, that in Rethinking Virtual Communities, the last chapter added to the 2000 edition of Virtual Communities, Rheingold writes:
"If I had encountered sociologist Barry Wellman and learned about social network analysis when I first wrote about cyberspace cultures, I could have saved us all a decade of debate by calling them 'online social networks' instead of 'virtual communities.'"[26]

Thanks to the application of graph theory to the study of small world social networks, by scientists such as Duncan Watts, Steven Strogatz and Albert-Laszlo Baribasi, group formations suddenly became something you could visualize on scales that were previously unheard of. Social patterns could be seen, quantified and tracked. There appeared to be topological explanations for how information spreads and why some groups flounder while others flourish. Social network analysis provides the framework to understand, the tools to visualize and the language to talk about large-scale group interaction. So, to better understand the big deal about small world networks and how that knowledge is shaping some of the decisions made by social software designers, particularly those designing software that is meant to turn online interactions into offline relationships, it's valuable to revisit the evolution of social network analysis (in a very small nutshell).

In 1967, social psychologist Stanley Milgram conducted an experiment to try and pose an answer to the "small world problem," a phrase that had been kicking around the social sciences sphere for a few years. Put simply, the small world problem asks, "Starting with any two people in the world, what is the probability that they will know one another?" Introducing a little more complexity into the question, while person one and person two may not know one another directly, they may be connected through a mutual acquaintance, or even a chain of mutual acquaintances. So, Milgram's hope was to be able to assign a numerical value to the average distance between any two people on earth, measured by the length of the chain of intermediate acquaintances that connected them. [27]

To test his hypothesis that any two people could be connected in such a way, and furthermore that the number of intermediate links required would in fact be quite small, Milgram conducted two studies in which a sample of people were asked to move a message toward a target person. Each person in the sample was instructed to pass the message forward to one person they knew on a first name basis that they thought would be more likely to know the target person. If the participant with the message in hand knew the target person, he forwarded it and the chain was completed. In the Kansas study, the wife of a Divinity School student living in Cambridge was the target person. In the Nebraska study, a stockbroker who worked in Boston and lived in Sharon, Massachusetts was the target person. When Milgram analyzed the data produced by the successfully completed message-passing chains, he concluded that the average number of intermediate acquaintances was 5.5 -- or, put in what are now familiar terms, the small world phenomenon says that there are only six degrees of separation between us all.[28]

Though it played a crucial role in laying the groundwork for what was to become the field of social network analysis, Milgram's experiment really raised more questions than it answered. Some became preoccupied with perceived problems with the parameters of the experiment itself: Does the fact that a farmer in Nebraska can reach a stock broker in Massachusetts through a short chain of intermediaries really imply that the same can be said of any two people in the world? Does a 20% rate of successfully completed chains constitute enough of a data sample to draw a meaningful conclusion? Does the fact that the experiment has yet to be successfully replicated throw the entire premise into question? Others were more interested in the primary paradox evinced by Milgram's results: Despite the fact that people form dense social circles with high clustering co-efficients (Watts and Strogatz' term for the likelihood that two people you know also know each other), we still have access to people in remote social spheres through chains of acquaintances. How can we be locally densely connected and yet also globally connected?

The idea that we are connected via short paths to anyone in the world feels wrong to us, because we only have access to a local perspective on the social network we take part in. In John Guare's play Six Degrees of Separation the character Ouisa marvels:
"...everybody on this planet is separated by only six other people. Six degrees of separation. Between us and everybody else on the planet. The president of the United States. A gondolier in Venice...It's not just the big names. It's anyone. A native in a rain forest. A Tierra del Fuegan. An Eskimo. I am bound to everyone on this planet by a trail of six people. It's a profound thought." [29]

It's profound and also hard to believe from the perspective of a single person. Given that I tend to socialize with the same group of people, and they tend to socialize with each other, how could I possibly be connected to someone halfway around the world with whom I have nothing in common? Mark Granovetter began to resolve this apparent paradox in his 1973 essay The Strength of Weak Ties. Granovetter characterizes strong ties as those which involve time, emotional intensity, intimacy and reciprocation, in other words, the people you are likely to count among your closest friends and family.[30] People connected by strong ties tend to form clusters that exhibit high levels of redundancy, meaning that the people you are closest to are highly likely to be close to each other as well. While this makes our social lives easier, it certainly does not help explain how we could be connected to strangers on other continents. Enter the weak tie.

Weak ties are acquaintances who are not part of your closest social circle, and as such have the power to act as a bridge between your social cluster and someone else's. Granovetter, in studying the ways in which people find new jobs, discovered that the weak social ties of casual acquaintances were almost always more instrumental in the process than were the strong ties of friends and families. Strong ties, he posited, might be predisposed to help you find a job, but they are rarely in a position to do so because they have access to the same information as you do. In a dense cluster of strongly tied people, information simply ricochets between group members until it eventually peters out; there's no way for the information to escape out of the small circle of friends and into the world at large. Weak ties, according to Granovetter, provide that escape hatch: "...whatever is to be diffused can reach a larger number of people, and traverse greater social distance (i.e. path length), when passed through weak ties rather than strong." [31]

So, small world networks can be locally clustered, but globally sparse, because some individuals are also serving as bridges between densely clustered local groups. Continuing developments in social network theory pointed to another characteristic of small world networks that clarify Granovetter's findings further: not all nodes are created equal, some are much better at creating bridges between clusters than others. Malcolm Gladwell, in his book The Tipping Point, calls these people who act as "supernodes" Connectors. According to Gladwell, Connectors have two distinguishing features. One of them is that they tend to be members of a diverse array of subcultures, social worlds and niches. The other is that they tend to exhibit a mastery of and a taste for cultivating weak tie relationships. He writes:
"Most of us, I think, shy away from this cultivation of acquaintances. We have our circle of friends, to whom we are devoted. Acquaintances we keep at arm's length....The purpose of making an acquaintance, for most of us, is to evaluate whether we want to turn that person into a friend; we don't feel we have the time or the energy to maintain meaningful contact with everyone. [Connectors are] quite different." [32]

Connectors are quite different, but as it turns out, they are also typical of every large and unconstrained social system. Highly connected supernodes are a reliable consequence of self-organization in complex systems, as proven by Barabasi's discovery that small world networks are characterized by a power-law degree distribution.[33] A power-law distribution basically indicates that 80% of the traffic is going to 1% of the participants in the network. Your worst high school fears have come true: whether or not talent, knowledge or skill is equally distributed, popularity is not. It hardly seems democratic, but the power-law is the pattern that reappears again and again in large open systems. We normally think of the elements of systems being distributed along a bell curve, where the mean, median and mode are all the same. SAT scores, for example, are represented by a bell curve, with the majority of scores falling solidly in the middle range and the number of people scoring higher than or lower than the average dropping off steadily on either side. A power-law distribution, however, creates a very different shape that is marked by a couple of very strange effects.

First, as the number of options in a small world network rises, the curve of the power law gets more precipitous, not less. Choice intensifies the curve rather than flattening it. As Clay Shirky writes in his paper Power Laws, Weblogs and Inequality, "Diversity plus freedom of choice creates inequality, and the greater the diversity, the more extreme the inequality."[34] Second, the majority of elements in a power law system are not average, as in a bell curve, but below average.[35] Plot the connections to nodes in a small world network and you'll find that nodes with a handful of connections are the norm, but that these profuse yet poorly connected nodes coexist with supernodes that are flooded with connections. These supernodes, or hubs, are powerful entities in small world networks and therefore play a significant role in keeping the network globally connected.

The small world network topology is powerful because it enables large groups to exhibit the desirable characteristics of having redundant connections, short paths between random members and a low overall clustering co-efficient.[36] In layman's terms, this means the network is robust (random removal of nodes won't bring it to its knees, though targeted removal of hubs is another story) and ideally suited for the diffusion of information throughout vast populations. It feels local, but it is global. But, how do path lengths, strong and weak ties, hubs, and power-laws effect the design of social software? In general, forging network ties is costly in terms of time and energy. Individuals have finite amounts of both. Social software is concerned with decreasing the transaction cost of connecting individuals to groups and groups to each other. If done right, social software can simultaneously expand an individual's social network, while providing them with the means to traverse the network efficiently. In this regard, the insight into the nature of information transmission and the complexity of human relations afforded by advances in social network analysis is extremely valuable. It does not, however, make the task simple.

As important as advances in the field of social network analysis have been to the design of social software, particularly social software attempting to leverage online interactions into offline relationships, understanding the features and topology of small world networks is not in and of itself enough. Some early attempts to harness the power of small world network topology, such as and FOAF, have already failed. There are still vital questions that need to be considered to make the most of what we know so far. What does it mean to be separated by six degrees? Is that a big or a small number? Do small degrees of separation matter if we don't know how to find the most efficient path to our desired target?[37] What's the best way to locate the most efficient path to a desired target? How does the fact that not all friendships are symmetrical skew network connections? How do you account for the fact that the notion of "friendship" is colored by social context (a friend you gossip with is not necessarily a friend you borrow money from)?[38] Perhaps the most interesting and complicated wrinkle is that social networks are dynamic.

The burgeoning field of social network analysis has provided us with the means for grasping, mapping and reflecting upon the dynamic social networks in which we take part. This is happening online and off. Friendster and Ryze make the online social networks created there visible in an effort to foster new relationships both virtual and real. is a company that uses social network metrics to map the flow of decision-making in business organizations in order to maximize information flow and innovation.[39] A new company called Visible Path has developed a Relationship Capital Management (RCM) platform that uses social network analysis to locate, leverage and create relationship capital.[40] Reflection, however, creates change. Granovetter notes, "...rather than take network structure as static and exogenous, it is important to look into how it is produced and reproduced by the details of everyday activity."[41] Since the structure of a social network is produced and reproduced as we participate in it, it is possible that the reflections created by advances in social network analysis will have profound effects upon the networks themselves in time. It is also possible that social software designed to seed real world relationships, such as Friendster, Ryze and Meetup, might one day play a profound role in the formation of our social infrastructure.

The Big Picture: Friendster, Ryze and Meetup >>>

[25] Brown and Duguid, The Social Life of Information, xvii.

[26] Rheingold, The Virtual Community: Homesteading on the Electronic Frontier, 359.

[27] Stanley Milgram, "The Small World Problem," Psychology Today, May 1967, 62.

[28] Ibid., 64.

[29] John Guare, Six Degrees of Separation: A Play, (New York: Vintage Books, 1990).

[30] Mark Granovetter, "The Strength of Weak Ties," American Journal of Sociology, v. 78, 1973, 1361.

[31] Ibid., 1366.

[32] Malcolm Gladwell, The Tipping Point: How Little Things Can Make a Big Difference, (Boston: Little, Brown and Company, 200), 45-46.

[33] Albert-Laszlo Barabasi, Linked: The New Science of Networks, (Cambridge: Perseus Press, 2002), 70.

[34] Clay Shirky, "Power Laws Weblogs and Inequalities," Clay Shirky's Writings About the Internet, 8 February 2003, ( (3 March 2003).

[35] Ibid.

[36] Duncan J. Watts, Small Worlds: The Dynamics of Networks between Order and Randomness, (Princeton: Princeton University Press, 1999), 240-242.

[37] Duncan J. Watts, Six Degrees: The Science of a Connected Age, (New York: W.W. Norton & Company, 2003), 132-136.

[38] Watts, Small Worlds: The Dynamics of Networks between Order and Randomness, 5.

[39], ( (3 March 2003).

[40] Antony Brydon, email correspondence with author, 7 April 2003.

[41] Mark Granovetter, Getting a Job: A Study of Contacts and Careers, (Chicago: University of Chicago Press, 1974),152.

Introduction  |  More is Different: Why "Social Software," Why Now?  |  Social Network Analysis: The Big Deal About Small Worlds  |  The Big Picture: Friendster, Ryze and Meetup  |  Case Study Analysis: Friendster, Ryze and Meetup  |  Directed Searching: Working the Network  |  Conclusions  |  Sources  |  Printer-friendly Version

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Contact: Alicia L. Cervini
Interactive Telecommunications Program, 2003