using social networks to understand and prevent substance use

SUBSTANCE USE amp MISUSE

Vol 39 Nos 10ndash12 pp 1685ndash1712 2004

Using Social Networks to

Understand and Prevent Substance Use

A Transdisciplinary Perspective

Thomas W Valente PhD Peggy Gallaher PhD

and Michele Mouttapa PhD

Department of Preventive Medicine Keck School of Medicine

University of Southern California Alhambra California USA

ABSTRACT

We review findings from research on smoking alcohol and other

drug use which show that the network approach is instructive for

understanding social influences on substance use A hypothetical

network is used throughout to illustrate different network findings

and provide a short glossary of terms We then describe how network

analysis can be used to design more effective prevention programs

and to monitor and evaluate these programs The article closes with

a discussion of the inherent transdisciplinarity of social network

analysis

Correspondence Thomas W Valente PhD Department of Preventive

Medicine Keck School of Medicine University of Southern California 1000

S Fremont Ave Building A-5133 Alhambra CA 91803 USA E-mail

tvalenteuscedu

1685

DOI 101081LSUM-200033210 1082-6084 (Print) 1532-2491 (Online)

Copyright amp 2004 by Marcel Dekker Inc wwwdekkercom

ORDER REPRINTS

Key Words Contextual factors Social networks Social network

analysis Peer influence

INTRODUCTION

Studies of human behavior have by and large focused on howindividual attributes correlate and sometimes cause certain outcomesFor example onersquos level of sensation seeking might be correlated withcriminal behavior Increasingly however scientists have begun to realizethat contextual factors (eg the social and physical environment)contribute significantly to variation in outcomes (Gorman et alin press Mason et al in press) This is particularly true with substanceuse related behaviors Social network analysis has emerged as animportant perspective that provides a way to study the social contextof substance use in a transdisciplinary manner

WHAT IS SOCIAL NETWORKS ANALYSIS

Social network analysis is a set of theories methods and techniquesused to understand social relationships and how these relationships mightinfluence individual and group behavior The theories used are typicallyembedded within other disciplines such as Anthropology Communica-tion Economics Psychology Sociology and many others The commonbasis for these theories is that individuals are influenced by the peoplethey have contact with and that individual positions within larger socialstructures can determine behavior (either through constraint orinfluence) The methods of social network analysis consist of establishedprocedures for measuring characteristics and dimensions of theserelationships and the specific mathematical algorithms (and associatedsoftware) used to operationalize network constructs (INSNA 2003)

There are two primary types of network data collection techniques

Egocentric techniques provide measures of a personrsquos local socialnetwork For example a researcher can ask respondents toprovide the first names of their closest friends and then askthe respondent to state whether each of these friends engagesin certain behaviors and whether the respondent engaged inthese behaviors with each friend (Valente and Vlahov 2001)These techniques are amenable to random sampling approaches

1686 Valente Gallaher and Mouttapa

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but do not generate a complete network that can be manipulatedmathematically to understand the networkrsquos structure

Sociometric techniques provide a measure of the entire socialnetwork by interviewing all members of the network Sociometrictechniques are most often used in small communities schoolsand organizations where the boundary of the network can bedefined Sociometric techniques are more powerful in the sensethat they provide a global view of the network and indicators forindividual positions in that network Sociometric data are anaggregate composition of egocentric data Sociometric techniquesalso require the use of specialty social network software such asUCINET or specific mathematical programming to calculatenetwork indicators (Analytic Technologies 2002) The ability togenerate a complete social network map of social relationsprovides considerable explanatory power

Sociometric social network measures are generated primarily attwo ecological levels individual and network There are hundreds ofindividual measures that can be generated from one social networkquestion For example asking students for the names of their friends atschool can generate hundreds of indicators measuring for each studenttheir relative centrality in the friendship network their membership ingroups reciprocity of ties and so on The same data can be used togenerate network level indicators such as the degree to which the networkis dense or sparse its centralization the number of cliques their overlapand so on Several good introductions to social network analysis arereadily available (Burt 1980 Marsden 1990 Monge and Contractor2002 Rogers and Kincaid 1981 Scott 2000 Wasserman and Faust1994)

This article reviews and discusses studies conducted to understandthe application of social network analysis to substance use To datestudies have primarily collected sociometric data a (near) completeenumeration of the study population This review will be presented in fiveparts First this article presents key social network definitions Secondthe article discusses how social networks contribute to understandingthe initiation and use of substances For example the degree to whichpeer substance use is associated with an individualrsquos own substance usewill be examined Third we discuss how to use social network analysisfor implementing interventions designed to prevent or reduce substancemisuse Although in its infancy this topic offers considerable promise foroptimizing the interactional process in substance misuse preventionprograms Fourth the article discusses the use of social network analysis

Social Networks 1687

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for program monitoring and evaluation Finally the article summarizesthe transdisciplinary nature of social network analysis and closes with asummary and recommendations for future directions

Figure 1 provides a graph a sociogram of a hypothetical socialnetwork This graph will be used to illustrate key points in the review thatfollows The network could be friendship ties among students in a schoolor communication among coworkers in an organization Although thisnetwork is small 20 nodes network analysis can be conducted on largenetworks (eg thousands of nodes) Note that these network relationscould also include weights indicating the strength of associationsfor example how close two people are And it can also include valencesie liking and disliking

SOCIAL NETWORK DEFINITIONS

Social network influences may operate at several levels includingthe individual level (eg onersquos positioning in the social network) andthe group level (eg network density) This section describes the various

1 2

8

9

7

6

5

4

3

20

10

1917

1615

1211

13 14

18

Figure 1 Hypothetical social network each circle represents a person and the

links between connections corresponding to friendship communication or other

network definitions In the language of sociometrics Fig 1 is referred to as a

lsquolsquographrsquorsquo each circle a node and the lines connecting them are referred to as

lsquolsquoedgesrsquorsquo


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social network variables that can be generated to explain adolescentsubstance use (see Table 1 and Glossary) Many studies have beenconducted in schools since the boundary is readily drawn though manycontexts could be chosen The school will be used here as the context forillustration One of the unique aspects of social network analysis is that aquestion that asks students to name their friends in a school can be usedto generate hundreds of different variables One class of variables consistsof measures of social position such as centrality bridges and isolates

Social Positions

One unique aspect of social network analysis is its ability tocategorize people in terms of their position in the network By nature oftheir social relations some people are more central or popular in a schoolthan others The most frequent and possibly useful position identifiedby social network analysis is centrality the degree a person occupiesa central position in the network Centrality can be measured at least adozen ways in the same network (Costenbader and Valente 2003Freeman 1979 Wasserman and Faust 1994)

The most intuitive measure of centrality is a count of the number oftimes a person is named in response to a network question Persons 3 5and 15 in Fig 1 would be considered the most central since they receivedthe most nominations four For example in a school-based friendshipnetwork students who are named as friends most frequently would beconsidered the most central students in the school and also consideredthe most popular There are several other key centrality measuresFor example betweenness is defined as the degree to which a person lieson the shortest paths connecting others in the network Closeness is theinverse of the average distance to others in the network Integrationis defined as the reverse distance to others in the network Eigenvector isthe first eigenvector of the matrix of ties Finally power is defined asthe weighted centrality scores of a personrsquos ties All of these measuresare standardized by the size of the network

Popular students are often selected as peer educators in preventionprograms regardless of whether popularity in the classroom is related totobacco or alcohol use (Terre et al 1992) Alexander and others (2001)showed that popular students those receiving the most ties were morelikely to smoke in schools with high smoking prevalence and less likely tosmoke in schools with low prevalence This finding indicates that peerleaders often exemplify the norms for their communities and are likely tobe earlier and perhaps heavier users of substances in communities where


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substance use is accepted Since leaders are often seen as influential and

harbingers of behavior they are often used to implement programs

a point we return to laterAnother position identified by social network analysis is liaison

A liaison is a person who connects otherwise disconnected or weakly

connected groups (Granovetter 1973) Liaisons are paradoxical in that

they are weakly connected to groups yet these weak connections give

them strength Granovetter (1973) referred to this as the strength of weak

ties because the ties were to people and groups which the person does not

see often or is not connected to through multiple channels Yet this

weakness is a strength because it gives the liaison access to information

and resources that the rest of the group do not have In Fig 1 Person 11

acts as a liaison between two otherwise disconnected groupsLiaisons may be at risk for substance use because they become

exposed to the norms of two different groups either of which may

support misuse (Ennett and Bauman 1993) Further liaisons often try

to fit in with a group that they are not strongly connected to and may

initiate substance use in order to conform to the grouprsquos norms On the

other hand since liaisons are not embedded within a group they may

be more resistant to group peer pressure Thus liaisons represent

a position that might or might not put them at risk depending on their

individual characteristics and the group dynamics within the network

The lack of adequate generalizable empirical findings in this area leaves

it as an area needing more research particularly transdisciplinarity (TD)

researchIsolates people connected to no one can also be identified by

network analysis In diffusion of innovations research isolates have been

shown to be later adopters of innovation because their position puts them

outside the flow of information about new ideas (Rogers 1995 Valente

1995) Similarly in a high-risk setting where substance use is high being

an isolate may offer protection because the individual is metaphorically

quarantined from negative influences in the group On the other hand

isolates are often strongly connected to another group and this other

group may put them at risk For example a middle school student

reporting no in-school friends and receiving no friendship nominations

may have their friends outside the school or in another school and these

ties may influence the student to use andor misuse substances Isolates in

Fig 1 are Persons 7 19 and 20 The increasing presence of electronic

communications cell phones internet and pagers has expanded social

networks immeasurably by removing spatial and temporaral syncronicity

as a necessary condition for communication


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Table 1 A short glossary of network terms (see Scott (2000) Valente (1995)

and Wasserman and Faust (1994) for more complete glossaries)

Term Definition

Nominations The choices a person makes in response to a network

question For example the people that a respondent

names in response to the question who are your friends

are that respondentrsquos nominations

Centrality The degree a person is centrally located in the network

There are at least a dozen different centrality measures

Reciprocity The degree a personrsquos nominations also nominate himher

Reciprocity can be direct or indirect (A nominates B

who nominates C who nominates A)

Network

exposure

The degree a personrsquos network engages in the behavior For

example the number or proportion of substance-using

friends is a personrsquos network exposure to substance use

Bridges or

liaisons

A person who links two or more otherwise disconnected

groups is a bridge

Group A set of people who are connected to one another at a rate

greater than to others in the network There are dozens

of ways to measure groups

Isolate A person not connected to anyone in the network

Density The volume of connections in the network Sparse networks

are those with low density

Transitivity Refers to whether connections between two people imply a

third connection

Distance The number of steps (links between two people) connecting

two people in the network For example the friend of a

friend is two steps away

Personal network

density

A measure of how many of onersquos friends are connected

Centralization The degree ties in a network are clustered around one or

a small group of people For example in a highly

centralized network most people nominate the same

person or the same few people as friends

Density The number of ties in a network as a proportion of those

possible Dense networks have many ties (links) while

sparse ones have few

Betweenness The degree to which a person lies on the shortest paths

connecting others in the network

Closeness The inverse of the average distance to others in the network

Integration The reverse distance to others in the network


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Network Measures

In addition to measures of peer influence and network positionnetwork analysis can also be used to create network measures thatcharacterize a personrsquos interpersonal environment For example reciproc-ity is the degree that the people a person names also name them A highdegree of reciprocity in a friendship network indicates a sharedunderstanding of who likes whom and hence a flat structure Lowlevels of reciprocity an asymmetric network could possibly indicatea hierarchical structure Person 1 has three of hisher nominationsreciprocated (Persons 2 3 and 5)

A second measure is personal network density the degree a personrsquosties are connected to one another A dense personal network indicatesthat a personrsquos friends know and like one another Dense personalnetworks can reinforce behavioral norms since once a behavior isaccepted by a majority of the group it is reinforced Person 1rsquos personalnetwork density is 25 (three links Persons 2 to 3 4 to 5 and 5 to 4of twelve possible see Fig 1 for clarification)

IMPLICATIONS FOR ADOLESCENT SUBSTANCE USE

Network level measures (centralization density transitivity) may alsodirectly or indirectly influence substance use Centralization is the degreenetwork ties are concentrated on one or few people More centralizednetworks may increase the effects of opinion leader behavior or moredense networks may increase the effects of prevalence overestimatesDensity is the number of ties in a network divided by the numberpossible Dense networks may accelerate behavioral diffusion since theremight be more peer modeling and peer influence Transitivity refers towhether connections between two people imply a third For examplePerson 1 nominated 4 and Person 4 nominated 5 so we expect in atransitive network that Person 5 will nominate 1 It is unclear howtransitivity may affect behavioral diffusion but possibly its effects aresimilar to those of density

Youth networks exist at school at home (including the neighbor-hood) and at other public places (church religious institutions clubs thepark) An individualrsquos network connections may be different in differentcontexts and may possibly influence drug use as well as nonusedifferentially Sociometric methods which require assessments of(nearly) all members of a network are not feasible in all contextshowever network analysis of high-risk contexts may be particularly


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informative Sussman and others (1998) found that the single mostfrequent location of drug use among high-risk adolescents was in theyouthrsquos bedroom with friends Neighborhood networks may bemeasurable and very informative regarding the etiology and topographyof teen drug use (Mason et al in press)

In his examination of multiple contexts Cook and others (Cooket al 2002) found that contexts tended to be correlated in terms of theirability to cause or support a range of adolescent behaviors Thus contextitself may act as a mediator or moderator of drug use behavior Researchon the consistency of an individualrsquos network position across contexts isan unexplored area of research although it is likely that networkpositions tend to become more stable across contexts with age Similarlymultilevel social network modeling is an unexplored but potentiallyfruitful method of understanding the determinants of adolescent druguse Many studies have demonstrated that social network variables andmeasures influence substance use (Neiagus et al 2001)

REVIEW OF SOCIAL NETWORKS AND

ADOLESCENT SUBSTANCE USE

This section briefly reviews previous studies that have utilized socialnetwork analysis to explain substance use among adolescents Theoriesthat are pertinent to these studies include a simple lsquolsquobirds of a feather flocktogetherrsquorsquo notion differential association theory and the theory ofreasoned action

The Influence of Peersrsquo Behaviors

lsquolsquoBirds of a Feather Flock Togetherrsquorsquo

This simple conception is not much more than an observation thatyouth tend to cluster together based on shared activities Indeed mostsubstance-use researchers would agree that people who misuse substancesare often surrounded by friends family members and associates that alsomisuse substances or tacitly approve of doing so There are numerousempirical examples Studies have shown that an individual adolescentrsquossubstance use is associated with and perhaps causally linked withsubstance use by their friends In the case of smoking for examplehaving a best friend who smokes (Urberg et al 1997) and having friendswho smoke (Alexander et al 2001 Aloise-Young et al 1994 Baumanand Ennett 1994 Botvin et al 1993 Flay et al 1994 Unger and


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Chen 1999 Urberg et al 1997) are associated with smoking Unger andChen (1999) provide longitudinal evidence that suggests that individualswho have friends who smoke are more likely to start smoking themselvesThere is cross-sectional evidence which suggests that among high schooladolescents peer involvement in illicit drug use (Rai et al 2003 Windle2000) and alcohol use (Windle 2000) are associated with onersquos owninvolvement in those behaviors Longitudinal evidence linking peer useand adolescent use also exists (Rice et al 2003) Other studies haveexamined the association between the number of friends who usesubstances and the individualrsquos substance use (Donato et al 1994 Meijeret al 1994 Wang et al 1997) There is evidence that the number offriends who use illicit drugs (Jenkins and Zunguze 1998) and smokecigarettes (Wang et al 1997) is positively associated with onersquos own illicitdrug use and smoking respectively

Alexander and others (2001) found that adolescents with a majorityof friends who smoke were almost twice as likely to smoke themselvesStudies have also found a positive association between smoking and theproportion of friends who smoke (Botvin et al 1993 Urberg et al1997) Referring to Fig 1 these results would suggest that if Persons 23 and 4 misuse substances then Person 1 would also likely misusesubstances

Social Learning and Differential Association Theories

The birds of a feather flock together notion is simplistic One wouldwant to understand the mechanisms underlying this network clusteringOne theory that might be invoked to provide an explanation of thisclustering is social learning theory Social learning theory (Bandura1986) posits that involvement in substance use is the result of modelingsignificant othersrsquo substance use and social reinforcement for initiatingsubstance use Social learning theory suggests that youth may gain aninterest in using drugs merely from watching others apparently receivingrewards of use This vicarious exposure and reward might consist ofcontact with the media (movies magazines) youth one observes from adistance in onersquos neighborhood or onersquos close friends Use is observed orinstructed and one might try a drug Another slightly different theory isone that suggests that exposure to drug use is a function of differentialassociation

Differential association theory (Sutherland and Cressey 1974) positsthat adolescents learn delinquent behavior such as substance use fromclose friends and family who also use substances themselves andor have


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favorable attitudes towards substance use Hence differential associationtheory suggests that associations with substance-using friends precedesactual substance use Youth may not tend to model such risky behaviorsfrom strangers or from impersonal influences according to a strictinterpretation of this model Ennett and Bauman (1994) provide supportfor this assumption They found that membership in a friendship groupwas associated with smoking Nonsmokers who associated with people incliques comprised of smokers were more likely to become smokers thanwere those who associated with people in nonsmoking cliques Friedmanand others (1997) showed that being connected to a large group of peoplewho use drugs is associated with drug use Sieving and others (2000)examined adolescent friendships longitudinally for three years anddemonstrated that over time higher levels of friendsrsquo drug use led toincreased alcohol use

Friendship GroupingmdashAntecedent or

Consequence of Substance Use

Both differential association theory and social learning theoryassume that adolescents use substances because they are influenced bytheir peersrsquo substance-using behaviors However the opposite may betrue students may select peers as friends based on similar patterns ofdelinquent behavior Donohew Clayton Skinner and Colon (1999) positthat individuals who are high on sensation seeking tend to select friendswho are also high on sensation seeking and are more likely to experimentwith alcohol marijuana and other substances Consistent with thisassertion Pearson and West (2003) used Markov models to show thatpeople who became substance users transitioned from belonging tononrisk-taking groups to risktaking-groups Analyses by Ennett andBaumann (1994) were conducted to determine whether the homogeneityof smoking behavior within groups was caused by social influence orfriendship selection based on smoking behavior This study wasmotivated in part by the need to understand how much of thecorrelation between adolescent smoking and that of their peers is due toinfluence and how much due to selection They conclude lsquolsquoAlthough ourfindings contradict the popular wisdom that peer group influence is largelyresponsible for adolescent smoking they substantiate previous research thatfound that both influence and selection processes contribute to smokinghomogeneity among peersrsquorsquo (Ennett and Bauman 1994) Kandelrsquos (1985)longitudinal study of high school students also found that models that


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included both selection and peer influence explained initiation intomarijuana use more fully than either factor alone

Other studies also have attempted to disentangle influence fromselection (Engels et al 1997 Fisher and Bauman 1988) Fisher andBauman (1988) looked at stable dyadic friendships and showed that thetwo became similar in their smoking behavior suggesting influence Onthe other hand dynamic friendship dyads also showed similarityindicating an effect of selection Engels and others (1997) also foundsupport for both influence and selection In a more recent study Urbergand others (1997) include assessments of the peer group behavior andfriendrsquos smoking reports They found that lsquolsquo the amount of influenceover the school year was very modest in magnitude and came from theclosest friend for initiation of usersquorsquo (Urberg et al 1997) A recent studyconducted in England by Michell and Amos (1997) showed that girls whobelonged to groups where smoking was common were more likely tosmoke Indeed the authors showed that girls began smoking in order toenhance their prestige in the community Many girls initiated smokingearly in order to demonstrate their status as being more matureReferring to Fig 1 these results would indicate that Persons 1 through 10are more likely to smoke if smoking is prevalent andor accepted in thisgroup In sum both influence and selection are responsible for thesimilarity of smoking behavior among friends and each suggests differentcausal mechanisms for how peers contribute to smoking onset andmaintenance

Perceptions of Peers Theory of Reasoned Action

The Theory of Reasoned Action in its simplest form posits that onersquosperceptions of (peer) social norms onersquos willingness to comply with thosenorms and onersquos expectations regarding the cost and benefits of engagingin the behavior will influence onersquos own intentions to act Theseintentions often lead to behaviors (Fishbein and Ajzen 1981) Thisconstellation of normative beliefs is strongly influenced by interactionwith peers Like social learning theory norms are learned from observingpeersrsquo behaviors andor hearing what their friends tell them In somecases the pervasive influence of peers leads to gross inaccuracies in onersquossocial perceptions of what is normative for different reference groupsFor example a person whose friends use certain substances may believeit is normative for everyone to use these substances even though onlya small percentage do


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Peer Norms

Theory of reasoned action states that behavior is influenced in part

by perceived peer norms (Fishbein and Ajzen 1981) One finding from

research on adolescents has been that youth often misuse substances

because they incorrectly believe that it is normative to use them

Adolescents have vastly overestimated the prevalence of smoking

drinking and drug consumption in their schools For example

Sussman and others (1988) found that regardless of smoking status

eighth and ninth graders overestimated weekly use of cigarettes among

youth their age However nonsmokers made gross underestimations and

regular smokers made overestimations of smoking among their age

group This finding has been used in substance use prevention programs

to lsquolsquore-normrsquorsquo these perceptions In their intervention study MacKinnon

and others (1991) found that changes in perceptions of friendsrsquo tolerance

of drug use was the most substantial mediator of program effects on drug

use In their review McCaul and Glasgow (1985) posit that smoking

prevention programs should communicate social consequences informa-

tion to peer leaders so they can change subjective norms and reduce the

smoking behavior of othersA research question one might ponder is whether youth overstate

drug use in general or whether youth base their general inaccurate

judgments of use on accurate perceptions of use by significant others

If youth overestimate drug use in general then one might confront youth

with actual use rates among persons they know or others in their physical

environment to instruct and correct the overestimate (and hence reduce

perceptions that one should use drugs because everybody else does)

However if overestimates of friendsrsquo drug use are accurate one would

need to expose these youth to wider social use norms and perhaps

counteract deviant influences among friends Iannotti and Bush (1992)

asked students to name their three closest friends and to state whether

they thought each smoked drank alcohol or used marijuana They

found that respondentsrsquo reports of their friends use did not correlate well

with those friendsrsquo self-reports Iannotti and Bush (1992) found that

perceptions of friendsrsquo use was more highly associated with self-reports of

use Similarly Rice and others (2003) found that high school student

responses to a general question regarding friendsrsquo use did not correlate

with the self-reports of their friends Perceptions of friendsrsquo use may

reflect projection and implicit cognitions (Stacy et al in this issue) rather

than environmental influence This distortion can be corrected by a

general overestimates correction followed by confrontation with positive


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nonusing social influence from friends and other peers (Sussman et al1988)

In a related study Valente and others (1997) found that women involuntary organizations in Cameroon misjudged their friendsrsquo contra-ceptive use They found however that perceived friendsrsquo use wasassociated with onersquos own use regardless of those perceptionsrsquo accuracyThere is a need to determine factors that are associated with discrepanciesbetween peer reports and self-reports of health-risk behaviors Onersquossocial networks thus may be created in part based on onersquosmisperceptions of onersquos clique members Alternatively one may alsoshare prosocial activities with onersquos clique members that might beidentified through assessment of multiple measures that might underlieclustering patterns

Perceived Social Consequences

There is also a need to distinguish between peer use and perceptionsof peer approval of use Many adolescents believe that desirable socialconsequences such as peer acceptance or peer support will occur asa result of using substances and are therefore motivated to initiatesubstance use For example Jenkins (2001) found that amongnonsubstance-using high school students peer pressure was the mostfrequently cited reason why refusing beer marijuana and drug use offerswas difficult Among continuation high school students Sussman andothers (1995) also found that perceived peer pressure was a reasonwhy at-risk students use drugs other than tobacco Furthermore otherstudies have shown that among high school boys drug use (Luthar andDrsquoAvanzo 1999) and cigarette smoking (Alexander et al 1999 Vegaet al 1996) are associated with peer acceptance of such use

Changes in Peer Networks as a Function of Age

It should be noted that the effects of peersrsquo behaviors perceived peernorms and perceived social consequences may differ among adolescentsof different ages due to changes in the structure and importance of peerrelationships during adolescence In a longitudinal study Feiring andLewis (1991) found that peer networks were larger by age 13 and thenumber of same-sex friends increased By high school age youth spendmore time away from adults and their lives become less dominatedby social interactions with small groups of same-sexed peer groups


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Concomitantly they become exposed to a wider range of unsupervisedsocial gatherings which consist more of interactions with crowds moredyadic relationships such as dating and more weak ties (liaisons) thanin earlier years (Dunphy 1963 Gavin and Furman 1989 Shrum andCheek 1987) Of course social-situational factors including same-sexpeer group relationships still remain quite important predictors oftobacco use across both junior high school and high school (Sussmanet al 1995)

However other findings suggest that the influence of specific friendsmay become attenuated during adolescence due the development ofonersquos self-concept For example Clark-Lempers Lempers and Ho (1991)suggest that the importance of various people including onersquos same-sexbest friend decreases from early to late adolescence Hence interventionstudies on adolescents should also target individual characteristics suchas motivation to resist substance use and social skills to resist substanceuse offers [see Sussman et al (in this issue)] The next section describeshow social network analysis can be used to design or augment substance-use prevention and treatment interventions

PREVENTION INTERVENTIONS

This section describes how previous studies have used the socialnetworks approach to implement substance-use prevention and inter-vention programs Early research on the diffusion of innovations showedthat opinion leaders can be effective health promoters Rogers andKincaid (1981) studied the adoption of family planning by rural Koreanwomen in the 1960s They discovered that the opinion leaders were earlyadopters of specific contraceptive methods while the rest of the villagewould later adopt the same contraceptive method

Latkin (1998) recruited street opinion leaders to communicate safeinjecting practices and showed that these opinion leaders adopted the safeinjecting messages themselves and effectively communicated it to othersOpinion leaders tend to be similar in most respects to the people theylead For example Moore and others (2004) recruited opinion leaders insubstance-user treatment facilities to train other counselors in theadoption of a new treatment protocol They found that opinion leaderswere similar to other counselors in terms of age education andexperience but were more knowledgeable about the treatment optionbeing adopted (lsquolsquodual diagnosisrsquorsquo in this case)

Broadhead and others (1998) developed a network method forrecruiting clients into treatment facilities to be educated on safe injection


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practices In their system existing clients are given vouchers to go out

and recruit new clients to come to the clinic and these new clients are

in turn used to recruit new clients from their network In this manner

the pool of clients grows through naturally occurring networks that

are larger and more diverse than could be obtained with traditional

outreachIn school-based studies there has been a long tradition of using

peer leaders to assist in program delivery Peer leaders are usually

chosen by asking students to write the names of other students who they

consider to be good leaders (Johnson et al 1986 Perry et al 2002)

The teacher collects the data and selects as peer leaders those

students who received the most nominations Valente and others (1999)

expanded this methodology to allow students to be assigned to a leader

they nominated or were closest to sociometrically Their study showed

that use of a sociometric method of selection was more effective than

leaders popularly chosen but placed in groups defined randomlyThese studies show that network data can be used to improve

program implementation yet there are many other possibilities For

example network data can be used to define subsets cliques or groups in

a network that can be targeted for intervention Often getting a group to

change behavior can be easier than trying to change people individually

since the group can reinforce the message and provide social support for

maintaining the new behavior (eg abstinence)The optimality algorithm used by Valente and others (2004) can be

improved by borrowing ideas from location science (the branch of

operations research devoted to understanding optimal locations for

emergency services warehouses etc) Social networks however are not

transportation networks and so algorithms will not be directly transfer-

able Still considerable research can and should be conducted to

determine optimal group formation techniques in the implementation

of health promotion and substance-use prevention programmingThese experiences have led to the creation of a substance-use

prevention program using social network analysis to structure work

groups The central research question is whether social network analysis

can be used to tailor an existing evidence-based prevention program to

increase its effectiveness The Transdisciplinary Drug Abuse Prevention

Research Center (TPRC) at USC has launched a program which adapts

Toward No Drug Abuse (TND) an evidence-based substance abuse

prevention program (Sussman in this issue) using social network

analysis The adaptation will include more group activities and social

network analysis will be used to define these groups Outcomes from this


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study will affect how programs should be created and hencedisseminated (Pentz et al in this issue)

POTENTIAL USES OF NETWORK DATA FOR

MONITORING AND EVALUATION

Few studies have been conducted that make explicit use of networkdata for program monitoring and evaluation because social networkanalysis tools have been readily available only recently This sectionsummarizes what has been done so far and what could be done in futurestudies Social network analysis reminds us that relations matter andpositions derived from those relations matter As such monitoring andevaluation plans may be augmented by assessing who is affected by anintervention For example a school-based program found to haveminimal impact may still be judged a success if key opinion leaders wereinfluenced In such a scenario the absolute (quantitative) changemeasured in the evaluation may be modest the qualitative change interms subsequent affects community norms through these leadershowever could be substantial

A second use of network analysis for evaluation is to show that aprogram changed the dynamics of peer influence For example in anearlier study it was shown that a media campaign was associated withchanged perceptions of network membersrsquo use of contraception (Valenteand Saba 2001) Similarly substance-use prevention programs cancollect network data on the perceived use of substances by specific peersand monitor whether these perceptions change

Finally network analysis can be used to disseminate evidence-basedprograms For example Lomas and others (1991) used network analysisto identify peer opinion leaders to institute a practice change in selectedhealth care settings Network analysis can be used to target disseminationactivities so that evidence-based and proven programs can be imple-mented more readily

Network analysis provides a technique to map specifically who hasadopted evidence-based programs and where they are in the networkThis then provides information on where and to whom to devoteadditional matched resources to accelerate this diffusion The networkmap provides important monitoring information indicating how well thepractice is spreading (Stoebenau and Valente 2003) Change agents canbe recruited to accelerate diffusion in social network groups lackingexisting adopters


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Transdisciplinarity

Network analysis is maturing as a field of inquiry and is now makingsubstantial contributions to research on health issues The computationaltools for managing relational data have matured to the point wherea community of users (the International Network for Social NetworkAnalysis) has formed to use network analysis to research a broad range oftopics Empirical evidence indicates that network concepts and variablesare important explanations for human behavior Network analysis offers atheoretically rich set of explanatory variables and procedures useful forunderstanding the etiology of substance use and means to prevent or treatit For example rather than seeing youth as solely a product of theirfamilial upbringing network analysis shows that there is a complexinteraction of adult and peer role models that influence behavior

The field of social network analysis seemed to foment independentlyin Anthropology Sociology and Psychology (Freeman 2000) and is nowinherently transdisciplinary Many network analysts have some mathe-matical training necessary for understanding the concepts and program-ming the measures This mathematical training is not always present withmost social scientists and it often limits network application to thosewith this interest or training Network analysis can be described asmathematical ethnography providing a deep description of a commu-nity in mathematically tractable ways Recent developments in graphtheory and computer science have aided the development of the networkfield considerably by providing tools needed to display and analyze largenetworks Network analysis then has no disciplinary home and relieson scanning journals from numerous fields to find application of themethod Interestingly network analysis is probably the best tool forstudying transdisciplinarity (Fuqua et al in this issue) The collaborationbehavior and citation behavior of individuals groups and fields can bestudied to measure the degree they interact with one another

The tradition in the social sciences has been to collect data onattributes of individuals and study associations between those attributesand outcomes Statistical programs such as SAS SPSS and STATAwere designed to treat individuals as atoms moving independentlyin their universe Social network analysis turns these notions on theirhead It assumes individuals are connected in molecular and organicstructures whose parameters have implications for human behaviorHow an individual attaches to that structure and how their actions maychange that structure provides a new means to understand why certainindividuals misuse substances and ways to develop programs to preventand treat it


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CONCLUSIONS

Network analysis has its limitations The sociometric technique isamenable to studies with populations in settings with natural boundaries(organizations schools small communities) and lacks some of thestrengths of random sample designs It also tends to quantify reducing toa number the complexity of interpersonal communication and relation-ships (not all ties are the same) Finally there are issues regardingappropriate statistical tests for nonindependent data as often used innetwork studies These limitations aside network analysis offers apromising new area of the causes consequences and potential treatmentsfor substance-use behavior

The contributions of network analysis have been delayed for decadesbecause social science disciplines focused on individual attributes [SociondashEconomic Status (SES) ethnicity gender] as key determinants ofbehavior While these factors are certainly important increasingly werecognize that peoplersquos social circles and their social capital (or lackthereof) are also key determinants of behavior The social networkapproach provides a rigorous yet versatile approach to studying thesepeer influences

This article reviewed studies that show that individual substance useand misuse is strongly associated with and perhaps influenced by use inonersquos social network By taking a social network approach severalresearch questions on the role of social and peer influences on substanceuse are suggested Further it was shown that positions in a socialnetwork (popularity liaisons isolates) are also associated with substanceuse and that they may interact with group and cultural level properties(Unger et al in this issue) Although it was omitted from the article weshould note that much of the effects described in this article would alsoapply to nonuse or abstinence Finally this article suggests that network-level properties such as density or centralization might influenceindividual use within those networks

The social network field has begun to mature as evidenced by thisrich body of research findings and its expanded application tointervention design program monitoring and evaluation and use as ameans to disseminate evidence-based programs The maturation how-ever is not without costs Increasingly it is difficult for those outside thefield to learn the specialized jargon software and culture of socialnetwork analysis Nonetheless the field is open to new discovery andapproaches Social network analysts are united by a common method(the focus on relationships rather than attributes) that transcendsdisciplinary boundaries The goal of this transdisciplinary team is to


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blend these perspectives and develop a more complete understanding ofthe factors that influence nonsubstance useabstinence substance useand ways to deter it

GLOSSARY

Differential association theory The theory posits that adolescentslearn delinquent behavior such as substance use from close friends andfamily who also use substances themselves andor have favorableattitudes towards substance use associations with substance-usingfriends precede actual substance use

Personal network density The degree a personrsquos ties are connected toone another

Social network analysis A set of theories methods and techniquesused to understand social relationships and how these relationships mightinfluence individual and group behavior

Social learning theory This theory posits that involvement insubstance use is the result of modeling significant othersrsquo substance useand social reinforcement for initiating substance use This theory suggeststhat youth may gain an interest in using drugs merely from watchingothers apparently receiving rewards of use

Theory of reasoned action This theory posits that onersquos perceptionsof (peer) social norms onersquos willingness to comply with those norms andonersquos expectations regarding the cost and benefits of engaging in thebehavior will influence onersquos own intentions to act These intentionsoften lead to behaviors

ACKNOWLEDGMENTS

Support for this project was provided by NIDA grant P50-DA16094


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THE AUTHORS

Thomas W Valente is an Associate

Professor in the Department of

Preventive Medicine Keck School of

Medicine and Director of the Master

of Public Health Program at the

University of Southern California He

received his PhD in Communication

from the Annenberg School for

Communication at USC in 1991 and

then spent nine years at the Johns

Hopkins University Bloomberg School

of Public Health He is author of

Evaluating Health Promotion

Programs (2002 Oxford University Press) Network Models of the

Diffusion of Innovations (1995 Hampton Press) and over 50 articles and

chapters on public health social networks behavior change and

program evaluation Valente uses social network analysis health

communication and mathematical models to implement and evaluate

health promotion programs primarily aimed at preventing substance

abuse tobacco use unwanted pregnancies and STDHIV infections

Peggy E Gallaher PhD is a Research

Associate in Preventive Medicine at the

University of Southern Californiarsquos

Keck School of Medicine Her research

interests include adapting psychosocial

measures written for adults for use

by children defining and assessing

acculturation and ethnic identity and

establishing the cultural equivalence of

psychological tests She received her

doctorate in Psychology from the

University of Texas at Austin in 1988

and a Masterrsquos degree in Biostatistics

from Columbia University in 1994


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Michele Mouttapa PhD received herPhD in Health Behavior Research atthe Institute for Health Promotion andDisease Prevention Research at theUniversity of Southern CaliforniaHer research interests include examin-ing the effects of sociocultural influ-ences on adolescent smoking Inaddition she is examining the influenceof individualism and collectivism onnumerous mediators related to healthoutcomes including peer susceptibilityand family values Mouttapa received

her MA in Psychology (with an emphasis in social-cognitive psychol-ogy) from California State University Fullerton in 1999

REFERENCES

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Alexander C Piazza M Mekos D Valente T W (2001) Peernetworks and adolescent cigarette smoking an analysis of thenational longitudinal study of adolescent health Journal ofAdolescent Health 2922ndash30

Aloise-Young P Graham J W Hansen W B (1994) Peer influenceon smoking initiation during early adolescence a comparison ofgroup members and group outsiders Journal of Applied Psychology79281ndash287

Analytic Technologies (2000) UCINET Version 5 Boston MABandura A (1986) Social Foundations of Thought and Action A Social

Cognitive Theory New Jersey Prentice HallBauman K E Ennett S T (1994) Peer influence on adolescent drug

use American Psychologist 49(9)820ndash822Botvin G J Baker E Botvin E M Dusenbury L Cardwell J

Diaz T (1993) Factors promoting cigarette smoking among blackyouth a causal modeling approach Addictive Behaviors18397ndash405

Broadhead R S Hechathorn D D Weakliem D L Anthony D LMadray H Mills R J Hughes J (1998) Harnessing peer


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networks as an instrument for AIDS prevention results from a

peer-driven intervention Public Health Reports 113(S1)42ndash57Burt R S (1980) Models of network structure Annual Review of

Sociology 679ndash141Clark-Lempers D S Lempers J D Ho C (1991) Early middle and

late adolescentsrsquo perceptions of their relationships with significant

others Journal of Adolescent Research 6296ndash315Cook T Herman M Phillips M Setterston R (2002) How

neighborhoods families peer groups and schools jointly affect

changes in early adolescent development Child Development

731283ndash1309Costenbader E Valente T W (2003) The stability of centrality when

networks are sampled Social Networks 25283ndash307Donato F Monarca S Chiesa R Feretti D et al (1994) Smoking

among high school students in 10 Italian towns patterns and

covariates International Journal of the Addictions 291537ndash1557Donohew L Clayton R R Skinner W F Colon S (1999) Peer

networks and sensation seeking some implications for primary

socialization theory Substance Use and Misuse 341013ndash1023Dunphy D C (1963) The social structure of urban adolescent peer

groups Sociometry 26230ndash246Engels R C M E Knibbe R A Drop M J de Haan Y T (1997)

Homogeneity of cigarette smoking within peer groups influence or

selection Health Education Behavior 24801ndash811Ennett S T Bauman K E (1993) Peer group structure and adolescent

cigarette smoking a social network analysis Journal of Health and

Social Behavior 34226ndash236Ennett S T Bauman K E (1994) The contribution of influence and

seletion to adolescent peer group homogeneity the case of

adolescent cigarette smoking Journal of Personality and Social

Psychology 67653ndash663Feiring C Lewis M (1991) The transition from middle-childhood

to early adolescence sex differences in the social network and

perceived self-competence Sex Roles 24489ndash509Fishbein M Ajzen I (1981) On construct validity a critique of miniard

and cohenrsquos paper Journal of Experimental Social Psychology

17340ndash350Fisher L A Bauman K E (1988) Influence and selection in the

friend-adolescent relationship findings from studies of adolescent

smoking and drinking Journal of Applied Social Psychology

18289ndash314


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Flay B R Hu F B Siddiqui O Day L E et al (1994) Differential

influence of parental smoking and friendsrsquo smoking on adolescent

initiation and escalation of smoking Journal of Health and Social

Behavior 35248ndash265Freeman L (1979) Centrality in social networks conceptual clarifica-

tion Social Networks 1215ndash239Freeman L (2000) Visualizing social networks Journal of Social

Structure 1 February 4 wwwcmuedujossFriedman S R Neaius A Jose B et al (1997) Sociometric risk

networks and risk for HIV infection American Journal of Public

Health 871289ndash1296Fuqua J Stokols D Gress J Phillips K Harvey Y (2004)

Transdisciplinary collaboration as a basis for enhancing the science

and prevention of substance use and misuse Substance Use and

MisuseGavin L A FurmanW (1989) Age differences in adolescentsrsquo perceptions

of their peer groups Developmental Psychology 25827ndash834Gorman D M Gruenewald P J Mezic I Waller L A Castillo-

Chavez C Bradley E Mezic J (2004) Implications of systems

dynamic models and control theory for environmental approaches

to the prevention of alcohol and other drug-related problems

Substance Use and MisuseGranovetter M (1973) The strength of weak ties American Journal of

Sociology 781360ndash1380Iannotti R J Bush P J (1992) Perceived vs actual friendsrsquo use of

alcohol cigarettes marijuana and cocaine which has the most

influence Journal of Youth and Adolescence 21375ndash389INSNA (2003) International Network for Social Network Analysis

httpwwwsfucainsnaJenkins J E (2001) Rural adolescent perceptions of alcohol and other

drug resistance Child Study Journal 31211ndash224Jenkins J E Zunguze S T (1998) The relationship of family structure

to adolescent drug use peer affiliation and perception of peer

acceptance of drug use Adolescence 33811ndash822Johnson C A Hansen W B Collins L M Graham J W (1986)

High school smoking prevention results of a three-year long-

itudinal study Journal of Behavioral Medicine 9439ndash452Kandel D (1985) On processes of peer influences in adolescent drug use

a developmental perspective Advances in Alcohol and Substance

Abuse 4139ndash163


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Latkin C (1998) Outreach in natural setting the use of peer leaders forHIV prevention among injecting drug usersrsquo networks PublicHealth Reports 113(S1)151ndash159

Lomas J Enkin M Anderson G M Hanna W J Vayda ESinger J (1991) Opinion leaders vs audit feedback to implementpractice guidelines delivery after previous cesarean sectionJournal of American Medical Association 2652202ndash2207

Luthar S S DrsquoAvanzo K (1999) Contextual factors in substance usea study of suburban and inner-city adolescents Development andPsychopathology 11845ndash867

MacKinnon D P Johnson C A Pentz M A Dwyer J H HansenW B Flay B R Wang E Y (1991) Mediating effects in aschool-based drug prevention program first-year effects of themidwestern prevention project Health Psychology 10164ndash172

Marsden P V (1990) Network data and measurement Annual Reviewof Sociology 16435ndash463

Mason M J Cheung I Walker L (2004) Substance use socialnetworks and the geography of risk and protection of urbanadolescents Substance Use and Misuse

McCaul K D Glasgow R E (1985) Preventing adolescent smokingwhat have we learned about treatment construct validity HealthPsychology 4361ndash387

Meijer R R Muijtjens AMM van der Vleuten CPM (1994)Nonparametric person-fit research some theoretical issues and anempirical example Applied Measurement in Education 977ndash89

Michell L Amos A (1997) Girls pecking order and smoking SocialScience Medicine 441861ndash1869

Monge P R Contractor N S (2002) Theories of CommunicationNetwork New York Oxford University Press

Moore K A Peters R H Hills H A LeVasseur J B Rich A RHunt W M Young M S Valente T W (in press)Characteristics of opinion leaders in substance abuse treatmentagencies Journal of Drug and Alcohol Dependence

Neiagus A Friedman S R Kottiri B J Des Jarlais D C (2001)HIV risk networks and HIV transmission among injecting drugusers Evaluation and Program Planning 24221ndash226

Pearson M West P (in press) Drifting smoke rings social networkanalysis and markov processes in a longitudinal study of friendshipgroups and risk-taking Connections 26

Pentz M A Mares D Schinke S Rohrbach L (2004) Policitalscience public policy and drug abuse prevention Substance Useand Misuse


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Perry C Williams C L Komro K A Veblen-Mortenson S et al

(2002) Project northland long-term outcomes of community action

to reduce adolescent alcohol use Health Education Research

17117ndash132Rai A A Stanton BWu Y Li X Galbraith J Cottrell L Pack R

Harris C DrsquoAlessandri D Burns J (2003) Relative influences of

perceived parental monitoring and perceived peer involvement on

adolescent risk behaviors an analysis of six cross-sectional data sets

Journal of Adolescent Health 33108ndash118Rice R E Donohew L Clayton R (in press) Peer network sensation

seeking and drug use among junior and senior high school

students Connections 26(2)32ndash58Rogers E M (1995) Diffusion of Innovations 4th ed New York The

Free PressRogers E M Kincaid D L (1981) Communication Networks A New

Paradigm for Research New York The Free PressScott J (2000) Social Network Analysis A Handbook 2nd ed Newberry

Park CA SageShrum W Cheek N H (1987) Social structure during the school years

onset of the degrouping process American Sociological Review

52218ndash223Sieving R Perry C Williams C (2000) Do friendships change

behaviors or do behaviors change friendships examining paths of

influence in young adolescentsrsquo alcohol use Journal of Adolescent

Health 2627ndash35Stacy A W Ames S Knowlton B (2004) Neurally plausible

distinction in cognition implications for drug abuse prevention

Substance Use and MisuseStoebenau K Valente T W (2003) The role of network analysis in

community-based program evaluation a case study from highland

madagascar International Family Planning PerspectivesSussman S (2004) A drug abuse theoretical integration a transdisci-

plinary speculation Substance Use and MisuseSussman S Dent C W Mestel-Rauch J Johnson C A Hansen

W B Flay B R (1988) Adolescent nonsmokers triers and

regular smokersrsquo estimates of cigarette smoking prevalence when

do overestimations occur and by whom Journal of Applied Social

Psychology 18537ndash551Sussman S Stacy AW Dent CW Simon T R Galaif E R Moss

M A Craig S Johnson C A (1995) Continuation high schools

youth at risk for drug abuse Journal of Drug Education 25191ndash209


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Sussman S Stacy A Ames S Freedman L (1998) Self-reportedhigh-risk locations of adolescent drug use Addictive Behaviors23405ndash411

Sutherland E H Cressey D R (1974) Criminology 9th edPhiladelphia PA Lippincott

Terre L Drabman R Meydrech E Hsu H (1992) Relationshipbetween peer status and health behaviors Adolescence 27595ndash602

Unger J B Chen X (1999) The role of social networks and mediareceptivity in predicting age of smoking initiation a proportionalhazards model of risk and protective factors Addictive Behaviors24371ndash381

Unger J B Baezconde-Garbanati L Shakib S Palmer PHNezami EMora J (2004) What are the implications of structural-cultural theoryfor drug abuse prevention Substance Use and Misuse

Urberg K A Degirmencioglu S M Pilgrim C (1997) Close friendand group influence on adolescent cigarette smoking and alcoholuse Developmental Psychology 33834ndash844

Valente T W (1995) Network Models of the Diffusion of InnovationsCresskill NJ Hampton Press

Valente T W Davis R L (1999) Accelerating the diffusion ofinnovations using opinion leaders The Annals of the AmericanAcademy of the Political and Social Sciences 56655ndash67

Valente T W Hoffman B R Ritt-Olson A Lichtman K JohnsonC A (2003) The effects of a social network method for groupassignment strategies on peer led tobacco prevention programs inschools American Journal of Public Health

Valente T W Jato M N Van der Straten A Tsitol L M (1997)Social network influences on contraceptive use among cameroonianwomen in voluntary associations Social Science and Medicine45677ndash687

Valente T W Saba W (2001) Campaign recognition and interpersonalcommunication as factors in contraceptive use in bolivia Journalof Health Communication 61ndash20

Valente T W Vlahov D (2001) Selective risk taking among needleexchange participants in baltimore implications for supplementalinterventions American Journal of Public Health 91406ndash411

Vega W A Apospori E Gil A G Zimmerman RS et al (1996)A replication and elaboration of the self esteem-enhancementmodel Psychiatry Interpersonal Biological Processes 59128ndash144

Wang M Q Fitzhugh E C Eddy J M Fu Q et al (1997) Socialinfluences on adolescentsrsquo smoking progress a longitudinalanalysis American Journal of Health Behavior 21111ndash117


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Wasserman S Faust K (1994) Social Networks Analysis Methods andApplications Cambridge UK Cambridge University Press

Windle M (2000) Parental sibling and peer influences on adolescentsubstance use and alcohol problems Applied Developmental Science498ndash110


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Key Words Contextual factors Social networks Social network

analysis Peer influence

INTRODUCTION

Studies of human behavior have by and large focused on howindividual attributes correlate and sometimes cause certain outcomesFor example onersquos level of sensation seeking might be correlated withcriminal behavior Increasingly however scientists have begun to realizethat contextual factors (eg the social and physical environment)contribute significantly to variation in outcomes (Gorman et alin press Mason et al in press) This is particularly true with substanceuse related behaviors Social network analysis has emerged as animportant perspective that provides a way to study the social contextof substance use in a transdisciplinary manner

WHAT IS SOCIAL NETWORKS ANALYSIS

Social network analysis is a set of theories methods and techniquesused to understand social relationships and how these relationships mightinfluence individual and group behavior The theories used are typicallyembedded within other disciplines such as Anthropology Communica-tion Economics Psychology Sociology and many others The commonbasis for these theories is that individuals are influenced by the peoplethey have contact with and that individual positions within larger socialstructures can determine behavior (either through constraint orinfluence) The methods of social network analysis consist of establishedprocedures for measuring characteristics and dimensions of theserelationships and the specific mathematical algorithms (and associatedsoftware) used to operationalize network constructs (INSNA 2003)

There are two primary types of network data collection techniques

Egocentric techniques provide measures of a personrsquos local socialnetwork For example a researcher can ask respondents toprovide the first names of their closest friends and then askthe respondent to state whether each of these friends engagesin certain behaviors and whether the respondent engaged inthese behaviors with each friend (Valente and Vlahov 2001)These techniques are amenable to random sampling approaches


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ACKNOWLEDGMENTS



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third connection




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18289ndash314


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third connection




Personal network

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third connection




Personal network

density














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groups is a bridge








third connection




Personal network

density














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Network Measures







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18289ndash314


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GLOSSARY






ACKNOWLEDGMENTS



ORDER REPRINTS

THE AUTHORS




































ORDER REPRINTS



REFERENCES










ORDER REPRINTS




























18289ndash314


ORDER REPRINTS



























Abuse 4139ndash163


ORDER REPRINTS
















ORDER REPRINTS































ORDER REPRINTS
















ORDER REPRINTS












ORDER REPRINTS






ORDER REPRINTS






ORDER REPRINTS

Peer Norms








































ORDER REPRINTS








ORDER REPRINTS








ORDER REPRINTS







































ORDER REPRINTS









ORDER REPRINTS

Transdisciplinarity





ORDER REPRINTS

CONCLUSIONS






ORDER REPRINTS


GLOSSARY






ACKNOWLEDGMENTS



ORDER REPRINTS

THE AUTHORS




































ORDER REPRINTS



REFERENCES










ORDER REPRINTS




























18289ndash314


ORDER REPRINTS



























Abuse 4139ndash163


ORDER REPRINTS
















ORDER REPRINTS































ORDER REPRINTS
















ORDER REPRINTS












ORDER REPRINTS






ORDER REPRINTS

Peer Norms








































ORDER REPRINTS








ORDER REPRINTS








ORDER REPRINTS







































ORDER REPRINTS









ORDER REPRINTS

Transdisciplinarity





ORDER REPRINTS

CONCLUSIONS






ORDER REPRINTS


GLOSSARY






ACKNOWLEDGMENTS



ORDER REPRINTS

THE AUTHORS




































ORDER REPRINTS



REFERENCES










ORDER REPRINTS




























18289ndash314


ORDER REPRINTS



























Abuse 4139ndash163


ORDER REPRINTS
















ORDER REPRINTS































ORDER REPRINTS
















ORDER REPRINTS












ORDER REPRINTS

Peer Norms








































ORDER REPRINTS








ORDER REPRINTS








ORDER REPRINTS







































ORDER REPRINTS









ORDER REPRINTS

Transdisciplinarity





ORDER REPRINTS

CONCLUSIONS






ORDER REPRINTS


GLOSSARY






ACKNOWLEDGMENTS



ORDER REPRINTS

THE AUTHORS




































ORDER REPRINTS



REFERENCES










ORDER REPRINTS




























18289ndash314


ORDER REPRINTS



























Abuse 4139ndash163


ORDER REPRINTS
















ORDER REPRINTS































ORDER REPRINTS
















ORDER REPRINTS












ORDER REPRINTS








ORDER REPRINTS








ORDER REPRINTS







































ORDER REPRINTS









ORDER REPRINTS

Transdisciplinarity





ORDER REPRINTS

CONCLUSIONS






ORDER REPRINTS


GLOSSARY






ACKNOWLEDGMENTS



ORDER REPRINTS

THE AUTHORS




































ORDER REPRINTS



REFERENCES










ORDER REPRINTS




























18289ndash314


ORDER REPRINTS



























Abuse 4139ndash163


ORDER REPRINTS
















ORDER REPRINTS































ORDER REPRINTS
















ORDER REPRINTS












ORDER REPRINTS








ORDER REPRINTS







































ORDER REPRINTS









ORDER REPRINTS

Transdisciplinarity





ORDER REPRINTS

CONCLUSIONS






ORDER REPRINTS


GLOSSARY






ACKNOWLEDGMENTS



ORDER REPRINTS

THE AUTHORS




































ORDER REPRINTS



REFERENCES










ORDER REPRINTS




























18289ndash314


ORDER REPRINTS



























Abuse 4139ndash163


ORDER REPRINTS
















ORDER REPRINTS































ORDER REPRINTS
















ORDER REPRINTS












ORDER REPRINTS







































ORDER REPRINTS









ORDER REPRINTS

Transdisciplinarity





ORDER REPRINTS

CONCLUSIONS






ORDER REPRINTS


GLOSSARY






ACKNOWLEDGMENTS



ORDER REPRINTS

THE AUTHORS




































ORDER REPRINTS



REFERENCES










ORDER REPRINTS




























18289ndash314


ORDER REPRINTS



























Abuse 4139ndash163


ORDER REPRINTS
















ORDER REPRINTS































ORDER REPRINTS
















ORDER REPRINTS












ORDER REPRINTS









ORDER REPRINTS

Transdisciplinarity





ORDER REPRINTS

CONCLUSIONS






ORDER REPRINTS


GLOSSARY






ACKNOWLEDGMENTS



ORDER REPRINTS

THE AUTHORS




































ORDER REPRINTS



REFERENCES










ORDER REPRINTS




























18289ndash314


ORDER REPRINTS



























Abuse 4139ndash163


ORDER REPRINTS
















ORDER REPRINTS































ORDER REPRINTS
















ORDER REPRINTS












ORDER REPRINTS

Transdisciplinarity





ORDER REPRINTS

CONCLUSIONS






ORDER REPRINTS


GLOSSARY






ACKNOWLEDGMENTS



ORDER REPRINTS

THE AUTHORS




































ORDER REPRINTS



REFERENCES










ORDER REPRINTS




























18289ndash314


ORDER REPRINTS



























Abuse 4139ndash163


ORDER REPRINTS
















ORDER REPRINTS































ORDER REPRINTS
















ORDER REPRINTS












ORDER REPRINTS

CONCLUSIONS






ORDER REPRINTS


GLOSSARY






ACKNOWLEDGMENTS



ORDER REPRINTS

THE AUTHORS




































ORDER REPRINTS



REFERENCES










ORDER REPRINTS




























18289ndash314


ORDER REPRINTS



























Abuse 4139ndash163


ORDER REPRINTS
















ORDER REPRINTS































ORDER REPRINTS
















ORDER REPRINTS












ORDER REPRINTS


GLOSSARY






ACKNOWLEDGMENTS



ORDER REPRINTS

THE AUTHORS




































ORDER REPRINTS



REFERENCES










ORDER REPRINTS




























18289ndash314


ORDER REPRINTS



























Abuse 4139ndash163


ORDER REPRINTS
















ORDER REPRINTS































ORDER REPRINTS
















ORDER REPRINTS












ORDER REPRINTS

THE AUTHORS




































ORDER REPRINTS



REFERENCES










ORDER REPRINTS




























18289ndash314


ORDER REPRINTS



























Abuse 4139ndash163


ORDER REPRINTS
















ORDER REPRINTS































ORDER REPRINTS
















ORDER REPRINTS












ORDER REPRINTS



REFERENCES










ORDER REPRINTS




























18289ndash314


ORDER REPRINTS



























Abuse 4139ndash163


ORDER REPRINTS
















ORDER REPRINTS































ORDER REPRINTS
















ORDER REPRINTS












ORDER REPRINTS




























18289ndash314


ORDER REPRINTS



























Abuse 4139ndash163


ORDER REPRINTS
















ORDER REPRINTS































ORDER REPRINTS
















ORDER REPRINTS












ORDER REPRINTS



























Abuse 4139ndash163


ORDER REPRINTS
















ORDER REPRINTS































ORDER REPRINTS
















ORDER REPRINTS












ORDER REPRINTS
















ORDER REPRINTS































ORDER REPRINTS
















ORDER REPRINTS












ORDER REPRINTS































ORDER REPRINTS
















ORDER REPRINTS












ORDER REPRINTS
















ORDER REPRINTS












ORDER REPRINTS












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