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Network Analysis in the Social Sciences Stephen P. Borgatti, et al. Science 323, 892 (2009); DOI: 10.1126/science.1165821 The following resources related to this article are available online at www.sciencemag.org (this information is current as of February 14, 2009 ): This article appears in the following subject collections: Sociology http://www.sciencemag.org/cgi/collection/sociology Information about obtaining reprints of this article or about obtaining permission to reproduce this article in whole or in part can be found at: http://www.sciencemag.org/about/permissions.dtl Science (print ISSN 0036-8075; online ISSN 1095-9203) is published weekly, except the last week in December, by the American Association for the Advancement of Science, 1200 New York Avenue NW, Washington, DC 20005. Copyright 2009 by the American Association for the Advancement of Science; all rights reserved. The title Science is a registered trademark of AAAS. Downloaded from www.sciencemag.org on February 14, 2009 Updated information and services, including high-resolution figures, can be found in the online version of this article at: http://www.sciencemag.org/cgi/content/full/323/5916/892 REVIEW terms, making it possible to objectively discover emergent groups in network data (5). Another front was the development of a program of laboratory experimentation on networks. Researchers at the Group Networks Laboratory at the Massachusetts Institute of Technology (MIT) began Stephen P. Borgatti, Ajay Mehra, Daniel J. Brass, Giuseppe Labianca studying the effects of different communication network structures on the speed and accuracy Over the past decade, there has been an explosion of interest in network research across the with which a group could solve problems (Fig. physical and social sciences. For social scientists, the theory of networks has been a gold mine, 2). The more centralized structures, such as the yielding explanations for social phenomena in a wide variety of disciplines from psychology to star structure, outperformed decentralized struceconomics. Here, we review the kinds of things that social scientists have tried to explain using tures, such as the circle, even though it could be social network analysis and provide a nutshell description of the basic assumptions, goals, and shown mathematically that the circle structure explanatory mechanisms prevalent in the field. We hope to contribute to a dialogue among had, in principle, the shortest minimum solution researchers from across the physical and social sciences who share a common interest in time (6). Why the discrepancy? Achieving the understanding the antecedents and consequences of network phenomena. mathematically optimal solution would have required the nodes to ne of the most potent ideas in the social execute a fairly complex sequence sciences is the notion that individuals are JN of information trades in which no embedded in thick webs of social relasingle node served as integrator of LW tions and interactions. Social network theory the information. But the tendency in provides an answer to a question that has preHL SR LS human networks seemed to be for occupied social philosophy since the time of the more peripheral members of a Plato, namely, the problem of social order: how network (i.e., the nodes colored blue autonomous individuals can combine to create SN in the “Star,” “Y,” and “Chain” netenduring, functioning societies. Network theory C12 C10 works in Fig. 2) to channel inforalso provides explanations for a myriad of social mation to the most central node (i.e., phenomena, from individual creativity to corpothe nodes colored red in Fig. 2), rate profitability. Network research is “hot” today, who then decided what the correct with the number of articles in the Web of Science LC HIL answer was and sent this answer on the topic of “social networks” nearly tripling HC back out to the other nodes. The in the past decade. Readers of Science are already RT ZR fastest performing network strucfamiliar with network research in physics and tures were those in which the disbiology (1), but may be less familiar with what DD tance of all nodes from the obvious has been done in the social sciences (2). HL HN FL integrator was the shortest (7). History The work done by Bavelas and C3 C5 his colleagues at MIT captured the In the fall of 1932, there was an epidemic of runaways at the Hudson School for Girls in up- Fig. 1. Moreno’s network of runaways. The four largest circles imagination of researchers in a numstate New York. In a period of just 2 weeks, 14 (C12, C10, C5, C3) represent cottages in which the girls lived. ber of fields, including psychology, girls had run away— a rate 30 times higher than Each of the circles within the cottages represents an individual political science, and economics. In the norm. Jacob Moreno, a psychiatrist, suggested girl. The 14 runaways are identified by initials (e.g., SR). All the 1950s, Kochen, a mathematician, the reason for the spate of runaways had less to nondirected lines between a pair of individuals represent feelings and de Sola Pool, a political sciendo with individual factors pertaining to the girls’ of mutual attraction. Directed lines represent one-way feelings tist, wrote a highly circulated paper, eventually published in 1978 (8), personalities and motivations than with the po- of attraction. which tackled what is known today sitions of the runaways in an underlying social network (3). Moreno and his collaborator, Helen of modeling the social sciences after the physical as the “small world” problem: If two persons are Jennings, had mapped the social network at Hudson ones was not, of course, Moreno’s invention. A selected at random from a population, what are using “sociometry,” a technique for eliciting and hundred years before Moreno, the social philos- the chances that they would know each other, graphically representing individuals’ subjective opher Comte hoped to found a new field of and, more generally, how long a chain of acquaintfeelings toward one another (Fig. 1). The links in “social physics.” Fifty years after Comte, the anceship would be required to link them? On the this social network, Moreno argued, provided French sociologist Durkheim had argued that basis of mathematical models, they speculated channels for the flow of social influence and ideas human societies were like biological systems in that in a population like the United States, at least among the girls. In a way that even the girls them- that they were made up of interrelated compo- 50% of pairs could be linked by chains with no selves may not have been conscious of, it was their nents. As such, the reasons for social regularities more than two intermediaries. Twenty years later, location in the social network that determined were to be found not in the intentions of individ- Stanley Milgram tested their propositions empiruals but in the structure of the social environ- ically, leading to the now popular notion of “six whether and when they ran away. Moreno envisioned sociometry as a kind of ments in which they were embedded (4). Moreno’s degrees of separation” (9). During this period, network analysis was also physics, complete with its own “social atoms” sociometry provided a way of making this abstract used by sociologists interested in studying the and its laws of “social gravitation” (3). The idea social structure tangible. In the 1940s and 1950s, work in social net- changing social fabric of cities. The common conLINKS Center for Network Research in Business, Gatton College works advanced along several fronts. One front viction at the time was that urbanization destroyed of Business and Economics, University of Kentucky, Lexington, was the use of matrix algebra and graph theory to community, and that cities played a central role in KY 40506–0034, USA. E-mail: sborgatti@uky.edu (S.P.B.), formalize fundamental social-psychological con- this drama. These sociologists saw concrete relaajay.mehra@uky.edu (A.M.), dbrass@uky.edu (D.J.B.), and cepts such as groups and social circles in network tions between people—love, hate, support, and so joe.labianca@uky.edu (G.L.) O 892 13 FEBRUARY 2009 VOL 323 SCIENCE www.sciencemag.org Downloaded from www.sciencemag.org on February 14, 2009 Network Analysis in the Social Sciences REVIEW www.sciencemag.org SCIENCE VOL 323 International Network for Social Network Analysis), 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 (23), public health (24), and crime/war fighting (25). 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. Of all the applied fields, national security is probably the area that has most embraced social network analysis. Crime-fighters, particularly those fighting organized crime, have used a network perspective for many years, covering walls with huge maps showing links between “persons of interest.” This network approach is often credited with contributing to the capture of Saddam Hussein. In addition, terrorist groups are widely seen as networks rather than organizations, fueling research on how to disrupt functioning networks (26). At the same time, it is often asserted that it takes a network to fight a network, sparking military experiments with decentralized units. Downloaded from www.sciencemag.org on February 14, 2009 on—as the basic stuff of community, and they nectedness (or density) of the family’s social used network analysis to represent community network. The more connected the network, the structure. For example, researchers interviewed more likely the couple would maintain a tradi1050 adults living in 50 northern Californian tional segregation of husband and wife roles, communities with varying degrees of urbanism showing that the structure of the larger network about their social relations (10). The basic pro- can affect relations and behaviors within the dyad. In the 1970s, the center of gravity of network cedure for eliciting network data was to get respondents (egos) to identify people (alters) with research shifted to sociology. Lorrain and White whom they had various kinds of relationships (18) sought ways of building reduced models of and then to also ask ego about the relationships the complex algebras created when all possible between some or all of the alters. They found that compositions of a set of relations were constructed urbanism did in fact reduce network density, (e.g., the spouse of the parent of the parent of …). which, in turn, was negatively related to psycho- By collapsing together nodes that were structurally logical measures of satisfaction and overall well- equivalent—i.e., those that had similar incoming being. A similar study of 369 boys and 366 girls and outgoing ties—they could form a new network between the ages of 13 and 19 in a Midwestern (a reduced model) in which the nodes consisted town of about 10,000 residents found that the of structural positions rather than individuals. adolescents’ behaviors were strongly influenced This idea mapped well with the anthropologists’ by the “cliques” to which they belonged (11). view of social structure as a network of roles The representation and analysis of community rather than individuals, and was broadly applicanetwork structure remains at the forefront of net- ble to the analysis of roles in other settings, such work research in the social sciences today, with as the structure of the U.S. economy (19). It was growing interest in unraveling the structure of also noted that structurally equivalent individuals computer-supported virtual communities that have faced similar social environments and therefore proliferated in recent years (12). Chain By the 1960s, the network perspective was thriving in anthropolWheel Y Circle ogy. Influenced by the pioneering work of Radcliffe Brown (13), there were three main lines of inquiry. First, at the conceptual level, anthropologists like S. F. Nadel began to see societies not as monolithic entities but rather as a “pattern or network (or ‘system’) of relationships obtaining between actors in Centralized Decentralized their capacity of playing roles relative to one another” (14). Second, Fig. 2. Four network structures examined by Bavelas and building on the insights of the an- colleagues at MIT. Each node represents a person; each line thropologist Levi-Strauss, scholars represents a potential channel for interpersonal communication. began to represent kinship systems The most central node in each network is colored red. as relational algebras that consisted of a small set of generating relations (such as could be expected to develop similar responses, “parent of” and “married to”) together with binary such as similar attitudes or behaviors (20). Another key contribution was the influential composition operations to construct derived relations such as “in-law” and “cousin.” It was soon strength of weak ties (SWT) theory developed by discovered that the kinship systems of such peoples Granovetter (21). Granovetter argued that strong as the Arunda of Australia formed elegant math- ties tend to be “clumpy” in the sense that one’s ematical structures that gave hope to the idea that close contacts tend to know each other. As a deep lawlike regularities might underlie the ap- result, some of the information they pass along is redundant—what a person hears from contact A parent chaos of human social systems (15, 16). Third, a number of social anthropologists began is the same as what the person heard from B. In to use network-based explanations to account for contrast, weak ties (e.g., mere acquaintances) can a range of outcomes. For example, a classic eth- easily be unconnected to the rest of one’s netnographic study by Bott (17) examined 20 urban work, and therefore more likely to be sources of British families and attempted to explain the con- novel information. Twenty years later, this work siderable variation in the way husbands and wives has developed into a general theory of social performed their family roles. In some families, capital—the idea that whom a person is connected there was a strict division of labor: Husband and to, and how these contacts are connected to each wife carried out distinct household tasks sepa- other, enable people to access resources that ultirately and independently. In other families, the mately lead them to such things as better jobs and husband and wife shared many of the same tasks faster promotions (22). By the 1980s, social network analysis had and interacted as equals. Bott found that the degree of segregation in the role-relationship of become an established field within the social husband and wife varies directly with the con- sciences, with a professional organization (INSNA, Social Network Theory Perhaps the oldest criticism of social network research is that the field lacks a (native) theoretical understanding—it is “merely descriptive” or “just methodology.” On the contrary, there is so much of it that one of the main purposes of this article is to organize and simplify this burgeoning body of theory. We will give brief summaries of the salient points, using comparisons with the network approach used in the physical sciences (including biology). Types of ties. In the physical sciences, it is not unusual to regard any dyadic phenomena as a network. In this usage, a network and a mathematical graph are synonymous, and a common set of techniques is used to analyze all instances, from protein interactions to coauthorship to international trade. In contrast, social scientists typically distinguish among different kinds of dyadic links both analytically and theoretically. For example, the typology shown in Fig. 3 divides dyadic relations into four basic types—similarities, social relations, interactions, and flows. Much of social network research can be seen as working out how these different kinds of ties affect each other. The importance of structure. As in the study of isomers in chemistry, a fundamental axiom of social network analysis is the concept that structure matters. For example, teams with the same composition of member skills can perform very differently depending on the patterns of relationships among the members. Similarly, at the level of the individual node, a node’s outcomes and 13 FEBRUARY 2009 893 REVIEW research area has been the prediction of similarity in time-toe.g., e.g., Other role Affective Cognitive Membership Attribute Location Kinship adoption of an innovation for e.g., e.g., e.g., e.g., Sex with Information e.g., e.g., e.g., pairs of actors (31). Performance Knows Likes Friend of Same Talked to Beliefs Same Same Mother of refers to a node’s outcomes with gender spatial clubs Knows Hates Boss of Advice to Personnel Sibling of respect to some good. For examand about Same Same temporal etc. Student of Helped Resources ple, researchers have found that attitude events space Sees as firm centrality predicts the firm’s Competitor of Harmed etc. happy etc. etc. ability to innovate, as measured etc. etc. by number of patents secured (32), Fig. 3. A typology of ties studied in social network analysis. as well as to perform well financially (33). Other research has future characteristics depend in part on its posi- formation of network ties and, more generally, to linked individual centrality with power and tion in the network structure. Whereas traditional predict a host of network properties, such as the influence (34). Theoretical mechanisms. Perhaps the most social research explained an individual’s outcomes clusteredness of networks or the distributions of or characteristics as a function of other character- node centrality. In the social sciences, most work common mechanism for explaining conseistics of the same individual (e.g., income as a of this type has been conducted at the dyadic quences of social network variables is some form function of education and gender), social network level to examine such questions as: What is the of direct transmission from node to node. Whether researchers look to the individual’s social environ- basis of friendship ties? How do firms pick alli- this is a physical transfer, as in the case of matement for explanations, whether through influence ance partners? A host of explanations have been rial resources such as money (35), or a mimetic processes (e.g., individuals adopting their friends’ proposed in different settings, but we find they (imitative) process, such as the contagion of ideas, occupational choices) or leveraging processes (e.g., can usefully be grouped into two basic categories: the underlying idea is that something flows along an individual can get certain things done because opportunity-based antecedents (the likelihood a network path from one node to the other. The adaptation mechanism states that nodes of the connections she has to powerful others). A that two nodes will come into contact) and benefitkey task of social network analysis has been to based antecedents (some kind of utility maximi- become homogeneous as a result of experiencing invent graph-theoretic properties that characterize zation or discomfort minimization that leads to tie and adapting to similar social environments. Much like explanations of convergent forms in structures, positions, and dyadic properties (such formation). Although there are many studies of network biology, if two nodes have ties to the same (or as the cohesion or connectedness of the structure) and the overall “shape” (i.e., distribution) of ties. antecedents, the primary focus of network research equivalent) others, they face the same environAt the node level of analysis, the most widely in the social sciences has been on the consequences mental forces and are likely to adapt by becoming studied concept is centrality—a family of node- of networks. Perhaps the most fundamental axiom increasingly similar. For example, two highly level properties relating to the structural impor- in social network research is that a node’s position central nodes in an advice network could develop tance or prominence of a node in the network. in a network determines in part the opportunities similar distaste for the telephone and e-mail, For example, one type of centrality is Freeman’s and constraints that it encounters, and in this way because both receive so many requests for help betweenness, which captures the property of fre- plays an important role in a node’s outcomes. This through these media. Unlike the case of transquently lying along the shortest paths between is the network thinking behind the popular con- mission, the state of “distaste for communication pairs of nodes (27). This is often interpreted in cept of social capital, which in one formulation media” is not transmitted from one node to terms of the potential power that an actor might posits that the rate of return on an actor’s invest- another, but rather is similarly created in each wield due to the ability to slow down flows or to ment in their human capital (i.e., their knowledge, node because of their similar relations to others. The binding mechanism is similar to the old distort what is passed along in such a way as to skills, and abilities) is determined by their social concept of covalent bonding in chemistry. The serve the actor’s interests. For example, Padgett capital (i.e., their network location) (29). Unlike the physical sciences, a multitude of idea is that social ties can bind nodes together in and Ansell (28) analyzed historical data on marriages and financial transactions of the powerful node outcomes have been studied as conse- such a way as to construct a new entity whose properties can be different from Medici family in 15th-century Florence. The study those of its constituent elements. suggested that the Medici’s rise to power was a Binding is one of the mechanisms function of their position of high betweenness behind the popular notion of the within the network, which allowed them to performance benefits of “structurbroker business deals and serve as a crucial hub al holes” (Fig. 4). Given an egofor communication and political decision-making. network (the set of nodes with direct Research questions. In the physical sciences, ties to a focal node, called “ego,” a key research goal has been formulating univertogether with the set of ties among sal characteristics of nonrandom networks, such Open Closed members of the ego network), a as the property of having a scale-free degree distribution. In the social sciences, however, researchers Fig. 4. Two illustrative ego networks. The one on the left structural hole is the absence of a tie have tended to emphasize variation in structure contains many structural holes; the one on the right contains few. among a pair of nodes in the ego network (22). A well-established across different groups or contexts, using these variations to explain differences in outcomes. For quences of social network variables. Broadly proposition in social network analysis is that example, Granovetter argued that when the city speaking, these outcomes fall into two main cat- egos with lots of structural holes are better perof Boston sought to absorb two neighboring egories: homogeneity and performance. Node formers in certain competitive settings (19). The towns, the reason that one of the towns was able homogeneity refers to the similarity of actors lack of structural holes around a node means that to successfully resist was that its more diffuse with respect to behaviors or internal structures. the node’s contacts are “bound” together—they network structure was more conducive to collective For example, if the actors are firms, one area of can communicate and coordinate so as to act as research tries to predict which firms adopt the one, creating a formidable “other” to negotiate action (21). A research goal that the social and physical same organizational governance structures (30); with. This is the basic principle behind the bensciences have shared has been to explain the similarly, where the nodes are individuals, a key efits of worker’s unions and political alliances. In 894 Social Relations 13 FEBRUARY 2009 VOL 323 Interactions SCIENCE Flows www.sciencemag.org Downloaded from www.sciencemag.org on February 14, 2009 Similarities REVIEW www.sciencemag.org SCIENCE VOL 323 This is one of many areas where we can each take lessons from the other. References and Notes 1. M. Newman, A. Barabasi, D. J. Watts, Eds., The Structure and Dynamics of Networks (Princeton Univ. Press, Princeton, NJ, 2006). 2. For a thorough history of the field, see the definitive work by Freeman (39). 3. J. L. Moreno, Who Shall Survive? Nervous and Mental Disease Publishing Company, Washington, DC, 1934). 4. E. Durkheim, Suicide: A Study in Sociology (Free Press, New York, 1951). 5. R. D. Luce, A. Perry, Psychometrika 14, 95 (1949). 6. H. Leavitt, J. Abnorm. Soc. Psychol. 46, 38 (1951). 7. Later experiments suggested that this result was contingent on other factors. For example, several experiments showed that, as the complexity of puzzles increased, decentralized networks performed better (40). 8. I. de S. Pool, M. Kochen, Soc. Networks 1, 1 (1978). 9. S. Milgram, Psychol. Today 1, 60 (1967). 10. C. S. Fischer, To Dwell Among Friends (Univ. of Chicago Press, Chicago, IL, 1948) 11. C. E. Hollingshead, Elmtown’s Youth (Wiley, London, 1949). 12. B. Wellman et al., Annu. Rev. Sociol. 22, 213 (1996). 13. R. Brown, Structure and Function in Primitive Society (Free Press, Glencoe, IL, 1952). 14. S. F. Nadel, The Theory of Social Structure (Free Press, Glencoe, IL, 1957). 15. H. White, An Anatomy of Kinship: Mathematical Models for Structures of Cumulated Roles (Prentice Hall, Engelwood, NJ, 1963). 16. J. P. Boyd, J. Math. Psychol. 6, 139 (1969). 17. E. Bott, Family and Social Network (Tavistock, London, 1957). 18. F. P. Lorrain, H. C. White, J. Math. Sociol. 1, 49 (1971). 19. R. S. Burt, Corporate Profits and Cooptation (Academic Press, NY, 1983). 20. R. S. Burt, Am. J. Sociol. 92, 1287 (1987). 21. M. S. Granovetter, Am. J. Sociol. 78, 1360 (1973). 22. R. S. Burt, Structural Holes: The Social Structure of Competition (Harvard Univ. Press, Cambridge, MA. 1992). 23. R. Cross, A. Parker, The Hidden Power of Social Networks (Harvard Business School Press, Boston, MA, 2004). 24. J. A. Levy, B. A. Pescosolido, Social Networks and Health (Elsevier, London, 2002). 25. M. Sageman, Understanding Terror Networks (Univ. of Pennsylvania Press, Philadelphia, 2004). 26. S. P. Borgatti, in Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers, R. Breiger, K. Carley, P. Pattison, Eds. (National Academy of Sciences Press, Washington, DC, 2003), p. 241. 27. L. C. Freeman, Sociometry 40, 35 (1977). 28. J. F. Padgett, C. K. Ansell, Am. J. Sociol. 98, 1259 (1993). 29. R. S. Burt, Brokerage and Closure (Oxford Univ. Press, New York, 2005). 30. G. F. Davis, H. R. Greve, Am. J. Sociol. 103, 1 (1997). 31. T. W. Valente, Soc. Networks 18, 69 (1996). 32. W. Powell, K. Koput, L. Smith-Doerr, Adm. Sci. Q. 41, 116 (1996). 33. A. V. Shipilov, S. X. Li, Adm. Sci. Q. 53, 73 (2008). 34. D. J. Brass, Adm. Sci. Q. 29, 518 (1984). 35. N. Lin, Annu. Rev. Sociol. 25, 467 (1999). 36. T. Yamagishi, M. R. Gilmore, K. S. Cook, Am. J. Sociol. 93, 833 (1988). 37. A. Giddens, The Constitution of Society (Univ. of California Press, Berkeley and Los Angeles, 1984). 38. D. Krackhardt, M. Kilduff, J. Pers. Soc. Psychol. 76, 770 (1999). 39. L. C. Freeman, The Development of Social Network Analysis: A Study in the Sociology of Science (Empirical Press, Vancouver, 2004). 40. M. E. Shaw, in Advances in Experimental Social Psychology, L. Berkowitz, Ed. (Academic Press, New York, 1964), vol. 1, p. 111. 41. We thank A. Caster, R. Chase, L. Freeman, and B. Wellman for their help in improving this manuscript. This work was funded in part by grant HDTRA1-08-1-0002-P00002 from the Defense Threat Reduction Agency and by the Gatton College of Business and Economics at the University of Kentucky. 10.1126/science.1165821 13 FEBRUARY 2009 Downloaded from www.sciencemag.org on February 14, 2009 value and not compared to expected values generated by a theoretical model such as Erdos-Renyi random graphs. For their part, social scientists have reacted to this practice with considerable bemusement. To them, baseline models like simple random graphs seem naïve in the extreme—like comparing the structure of a skyscraper to a random distribution of the same quantities of materials. More importantly, however, social and physical scientists tend to have different goals. In the physical sciences, it has not been unusual for a research paper to have as its goal to demonstrate that a series of networks have a certain property (and that this property would be rare in random networks). For social scientists, the default expectation has been that different networks (and nodes within them) will have varying network properties and that these variations account for differences in outcomes for the networks (or nodes). Indeed, it is the relating of network differences to outcomes that they see as constituting theoretical versus descriptive work. Social scientists have also been a c e b d more concerned than the physical scientists with the individual node, Fig. 5. A five-person exchange network. Nodes represent whether an individual or a collecpersons; lines represent exchange relations. tive such as a company, than with Having only strong nodes to bargain with makes the network as a whole. This focus on node-level node c weak. In this way, a node’s power be- outcomes is probably driven to at least some comes a function of the powers of all other nodes extent by the fact that traditional social science in the network, and results in a situation in which theories have focused largely on the individual. a node’s power can be affected by changes in the To compete against more established social scinetwork far away from the node. An example of ence theories, network researchers have had to the exclusion mechanism occurs in business-to- show that network theory can better explain the business supply chains. When a firm intentionally same kinds of outcomes that have been the tralocks up a supplier to an exclusive contract, ditional focus of the social sciences. Some physicists argue that direct observation competitor firms are excluded from accessing that supplier, leaving them vulnerable in the of who interacts with whom would be preferable to asking respondents about their social contacts, marketplace. In quantum physics, the Heisenberg uncer- on the grounds that survey data are prone to error. tainty principle describes the effects of an observer Social scientists agree that survey data contain on the system being measured. A foreseeable chal- error, but do not regard an error-free measurement lenge for network research in the social sciences is of who interacts with whom to be a substitute for, that its theories can diffuse through a population, say, who trusts whom, as these are qualitatively influencing the way people see themselves and different ties that can have different outcomes. In how they act, a phenomenon that Giddens de- addition, social scientists would note that even scribed as the double-hermeneutic (37). For exam- when objective measures are available, it is often ple, there has been an explosion in the popularity of more useful for predicting behavior to measure a social networking sites, such as Facebook and person’s perception of their world than to measure Linkedin, which make one’s connections highly their actual world. Furthermore, the varying abilvisible and salient. Many of these sites offer users ity of social actors to correctly perceive the netdetailed information about the structure and con- work around them is an interesting variable in tent of their social networks, as well as suggestions itself, with strong consequences for such outcomes for how to enhance their social networks. Will this as workplace performance (38). enhanced awareness of social network theories It is apparent that the physical and social alter the way in which people create, maintain, and sciences are most comfortable at different points leverage their social networks? along the (related) continua of universalism to particularism, and simplicity to complexity. From Final Observations a social scientist’s point of view, network research A curious thing about relations among physical in the physical sciences can seem alarmingly simand social scientists who study networks is that plistic and coarse-grained. And, no doubt, from each camp tends to see the other as merely de- a physical scientist’s point of view, network rescriptive. To a physical scientist, network research search in the social sciences must appear oddly in the social sciences is descriptive because mea- mired in the minute and the particular, using tiny sures of network properties are often taken at face data sets and treating every context as different. contrast, a node with many structural holes can play unconnected nodes against each other, dividing and conquering. The exclusion mechanism refers to competitive situations in which one node, by forming a relation with another, excludes a third node. To illustrate, consider a “chain” network (Fig. 5) in which nodes are allowed to make pairwise “deals” with those they are directly connected to. Node d can make a deal with either node c or node e, but not both nodes. Thus, node d can exclude node c by making a deal with node e. A set of experiments (36) showed that nodes b and d have high bargaining power, whereas nodes a, c, and e have low power. Of special interest is the situation of node c, which is more central than, and has as many trading partners as, nodes b and d. However, nodes b and d are stronger because each have partners (nodes a and e) that are in weak positions (no alternative bargaining partners). 895