The Early Beginnings of Social Network Analysis and the Potential Use for Homeland Security

Over the last few years we have seen an explosion of activity related to social network analysis. It has been used in to look at a variety of issues such as business practices, transportation, education, healthcare, and homeland security and defense.

In this blog post, I want to discuss how social network analysis grew from its humble initial application in education to be one of the most powerful tools for examining the social dynamics of any network of people.

Diane Harris Cline, The Social Network of Alexander the Great

One of the most potent ideas in the social sciences is the notion that individuals are embedded in thick webs of social relations and interactions. Social network theory provides an answer to a question that has preoccupied social philosophy since the time of Plato, namely, the problem of social order: how autonomous individuals can combine to create enduring, functioning societies. In those groups, the contacts and influences with and among individuals have powerful consequences that often influence behavior.

Historically, social network analysis was used to understand the social influence of individuals upon each other, which has the potential to change the social system and subsequent behavior. Social network analysis and social influence had very humble beginnings; with a group of runaway school girls.

The Humble History of Social Network Analysis, 1932

In the fall of 1932, there was an epidemic of runaways at the Hudson School for Girls in upstate New York. In a period of just two weeks, 14 girls ran away from school— a rate 30 times higher than the norm. Jacob Moreno, a psychiatrist, suggested the reason for the spate of runaways had less to do with individual factors pertaining to the girls’ personalities and motivations than with the positions of the runaways in an underlying social network. Moreno and his collaborator, Helen Jennings, mapped the social network at Hudson using “sociometry,” a technique for eliciting and graphically representing individuals’ subjective feelings towards one another. By examining the links in this social network, Moreno and Jennings argued that there were channels for the flow of social influence and ideas among the girls. In a way that even the girls themselves may not have been conscious of, it was their location in the social network that determined whether and when they ran away. In the sociogram displayed, Moreno and Jennings draw out the network of runaways. The four largest circles (C12, C10, C5, C3) represent cottages in which the girls lived. Each of the circles within the cottages represents an individual girl by their initials. All non‐directed lines between a pair of individuals represent feelings of mutual attraction. Directed lines represent one-way feelings of attraction.

Six Degrees of Separation is basically how LinkedIn works.

1940’s and 1950's

In the 1940’s and 1950’s social network analysis advanced under several fronts, most notably under the leadership of Alex Bavelas, a team of researchers at the Group Networks Laboratory at MIT began studying the effects of different communication network structures on the ability of groups to solve problems. The work done by Bavelas and his colleagues at MIT captured the imagination of researchers in a number of fields, including psychology, political science, and economics. In the 1950s, Kochen, a mathematician, and de Sola Pool, a political scientist, wrote a highly circulated paper, eventually published in 1978, which tackled what is known today as the “small world” problem. They asked the question: If two persons are selected at random from a population, what were the chances that they would know each other, and, more generally, how long a chain of acquaintanceship would be required to link them? On the basis of mathematical models, they speculated that in a population like the United States, at least 50% of pairs could be linked by chains with no more than two intermediaries. Twenty years later, Stanley Milgram tested their propositions empirically, leading to the now popular notion of “six degrees of separation.”

1970’s and 1980's

By the 1970’s and 1980’s, social network analysis had become an established field within the social sciences, with a professional organization (INSNA), an annual conference (SUNBELT), specialized software (e.g., UCINET) and its own journal (Social Networks). In the 1990s, network analysis radiated into a great number of fields, including physics and biology. It also made its way into several applied fields such as management consulting, public health, and crime/war fighting. In management consulting, network analysis is often applied in the context of knowledge management, where the objective is to help organizations better exploit the knowledge and capabilities distributed across its members. In public health, network approaches have been important both in stopping the spread of infectious diseases and in providing better health care and social support.

Cohesive Group Sociology: “Contacts and Influences”

The notion of a cohesive group is foundational in sociology. Early sociologists talked about little else. Their work provided an intuitive “feel” for groups, but it did not define groups in any systematic way. When the social network perspective emerged, however, network analysts set out to specify groups in structural terms. Freeman and Webster described the observation behind this structural perspective on groups:

“…..whenever human association is examined, we see what can be described as thick spots — relatively unchanging clusters or collections of individuals who are linked by frequent interaction and often by sentimental ties. These are surrounded by thin areas — where interaction does occur, but tends to be less frequent and to involve very little if any sentiment.”

An early social network analyst, George Homans spelled out the intuitive basis for the social network conception of cohesive groups by defining the interactions of its members. An article written by Watts and Strogatz in 1998 addressed a standard topic in social network analysis, called the “small world.” Concern with that issue stemmed from one of the classic social network papers, “Contacts and influences,” written by Ithiel de Sola Pool and Manfred Kochen in the mid-1950s. The questions raised by Pool and Kochen concerned patterns of acquaintanceship linking pairs of persons. They speculated that any two people in the United States are linked by a chain of acquaintanceships involving no more than seven intermediaries.

Various students picked up on Pool and Kochen’s ideas, including Stanley Milgram who used them as the basis for his doctoral dissertation on the “small world.” Milgram published several papers on the subject, one of which one was a popularization that appeared in Psychology Today in 1967. In these kinds of scenarios individuals can be influenced by the egos and social mores of the group and individuals within the group.

Perhaps the most common mechanism in social network analysis is some form of direct transmission from node to node (people to people). This transfer can be of one of a set of ideas or of ego strength transfer but the underlying idea is that something flows along a network path from one node to the other. Thus, a pair of nodes (people) may exhibit similar attitudes or behavior because one node (person) has influenced the other, either directly or through a path of intermediaries. The strength of a node means that the node’s contacts are “bound” together — they can communicate and coordinate so as to act as one, creating a formidable ‘other’ to negotiate with. This is the basic principle behind groups such as the benefits of worker’s unions, political and religious alliances. In contrast, a node also can play unconnected nodes against each other, dividing and conquering. It is also the basic principle behind the notion of an agent — someone who is “bound’ to or acts in the interests of another.

Think of how this plays out in the decentralization of ISIS today and its social influence of people all over the world through social media. And, additionally, the ability of fear to spread among social networks when primed by the media, politicians, religious leaders or outside forces.

But, to see how this has played out in the past, let’s look at these dynamics using a famous study in health which took place over several years called the Framingham Study.

Obesity Influence and Social Network Analysis: The Framingham Study

The growing obesity epidemic provides fertile ground for applying social network analysis as a tool for understanding how relationships impact health. The New England Journal of Medicine published a fascinating article about social network analysis as a tool for understanding obesity. This article is of particular significance because obesity is a multifaceted problem with underlying causes for which there are no easy answers. The research involved a quantitative analysis of the nature and extent of the person-to-person spread of obesity as a possible factor contributing to the obesity epidemic. This longitudinal case study utilized body-mass index data available on 12,067 participants and their “ties” in the famed Framingham Study over the years 1971–2003. The goal was to evaluate whether individual weight gain was a product of weight gain in close associations such as friends, siblings, and spouses.

Figure 1

Figure 1 examines the obesity association at a point in time-the year 2000. There were clearly visible clusters of obese persons in the network demonstrated by centrality-the distance of each node to all others in the graph. The directed and undirected binary ties are strong enough to serve as a bridge between clusters and the density of the yellow clusters (obese persons) indicates tie strength within a cluster but not necessarily between clusters of obese persons. Although this is a social network analysis for a point in time, the strength of these relationships is evident in that a person’s chances of becoming obese increased if he or she had a friend, sibling, or spouse who was obese. Fortunately, the Framingham data is longitudinal and allows for detailed comparisons over time.

Figure 2 provides a longitudinal history of the Obesity epidemic by looking at relationships in a closed social network over time. In each slide, the risk of obesity among associates who were connected to an obese individual-whose behavior was under study in this non-random network- was higher. The significant finding is that obesity may spread from person to person similar to a contagious disease.

Figure 2 also demonstrates the “spreading” is dependent upon the relationship. For example, the study indicates that friendships play a greater role in “spreading” obesity than does being married to an obese person or having obese neighbors.

It is possible to look at this longitudinal data and conclude that obesity is becoming increasingly acceptable within a variety of social circles. The fact that social relationships contribute to spreading obesity is important for understanding how to develop public health interventions that might reduce the obesity epidemic. Knowing which relationships have more influence on spreading obesity provides a starting point for spreading positive health behaviors. The modification of personal social networks has great potential for applying powerful public health interventions. Efforts to understand the interactions between the cellular, disease, and social networks are part of network medicine, which aims to quantify the complex interlinked factors that may contribute to individual diseases. Researchers found that people were most likely to become obese when a friend became obese. That increased a person’s chances of becoming obese by 57 percent. Proximity did not seem to matter, in fact the influence of the friend remained even if the friend was hundreds of miles away. And the greatest influence of all was between mutual close friends. If one became obese, the odds of the other becoming obese were nearly tripled.

Now think about how this social influence of the social network can influence those who might do harm in a terrorist network, even miles or countries apart.

Implications for Homeland Security

One of the reasons social media and social networks have grown so fast is that it taps into what people, as human beings, naturally love and need and want to do: create, share, connect, and relate. Humans have the ability to self-organize. Technology in general, and especially social technology, can shift relationships and disrupt traditional power structures. Social technology has brought an awareness of how networks form in social groups and that they are powerful influencers of human behavior in many ways.

According to Fass in his book, Next Generation Homeland Security, America cannot be returned to an agrarian democracy or the industrial age. However, a classical American bottom-up resilience for the information age can be restored. The information age is about networks. Networks empower people. A nation with a self-reliant, empowered citizenry makes not for a single point of failure but rather fosters a competitive advantage and continued global engagement.

Figure 1. Vladis Krebs, Social Network Analysis, 9/11 Terrorist Team

To put this proposition in very personal, American terms, if the homeland security enterprise had understood social networks, it may have been quite possible to connect the dots to prevent 9/11. One management consultant, Valdis Krebs, scoured newspaper reports to build up a social network of the 19 terrorists that he could begin to analyze. Within weeks, his network began to create a visual and mathematical picture of the links between the terrorists.

Yet, the same kinds of understanding network dynamics for destruction can be used to understand network dynamics for community strengths and resiliency. Over the last few years we have seen an explosion of activity related to social network analysis. It has been used in to look at a variety of issues such as business practices, transportation, education, healthcare, and homeland security and defense. Steve Ressler has written a comprehensive article on how social network analysis can be used to combat terrorism.

The early history regarding social network analysis began by looking at how the complex interactions between individuals in a group influenced the behavior of the group. As shown in one case example involving social networks effects on obesity, the understanding and examination of social networks has powerful implications for all types of complex problems. Social network analysis has been and will continue to be a powerful and insightful way to examine the dynamic interactions of diverse groups, for good or bad.

Angi English has a Master’s in Security Studies from the Naval Postgraduate School’s Center for Homeland Defense and Security and a Master’s in Educational Psychology from Baylor University. She lives in Austin, Texas.

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