A Cautionary Note on Data Inputs and Visual Outputs in Social Network Analysis

Methodology Corner

A Cautionary Note on Data Inputs andVisual Outputs in Social Network Analysis

Steve ConwayUniversity of Bath, School of Management, Bath BA2 7AY, UK

Email: [email protected]

Innovations in network visualization software over the last decade or so have beenimportant to the popularization of social network analysis (SNA) among academics,consultants and managers. Indeed, there is a growing literature that seeks to demon-strate how ‘invisible social networks’ might be revealed and leveraged for ‘visible results’through management interventions. However, the seductive power of the networkgraphic has distracted attention away from a variety of emerging and long recognizedconcerns in SNA. For example, weaknesses exist in data collection techniques that oftenrely on nominal boundary-setting and respondent recall. Non-response can also be highlyproblematic. Increasingly, email data are being employed, yet this represents a poorproxy for relationships and raises issues of privacy. In displaying relational data, visu-alizations typically reify and ossify the network. Yet, individual perceptions of a networkcan vary greatly from unified visualizations, and their structure is typically fleeting. Theaim of this paper is to draw together the diffuse literature concerning data input andvisual output issues in SNA, in order to raise awareness among management researchersand practitioners. In doing so, the nature and impact of such weaknesses are discussed,as are ways in which these might be resolved or mitigated.

Introduction

The network literature has grown exponentially inrecent years across a wide range of fields, includingbusiness and management (Borgatti and Foster,2003, p. 992). A key approach adopted in thisliterature is that of social network analysis (SNA)(e.g. Ahuja and Carley, 1999; Allen, James andGamlen, 2007; Cantner and Graf, 2006; Casper,2007; Cattani and Ferriani, 2008; Cross, Borgattiand Parker, 2002; Kijkuit and van den Ende,2010). It is argued that the emergence over the last10–15 years of powerful and freely available

network visualization tools (e.g. Krackplot,1

UCINET,2 Payek,3 Metasight4) has encouragedthe use of SNA techniques by management aca-demics, and fuelled their popularization amongbusiness consultants and managers.5 Indeed, thereis a growing literature that seeks to demonstratehow ‘invisible social networks’ might be revealed

Early drafts of this paper were presented at a departmen-tal seminar at the University of Leicester School of Man-agement and at the 28th SCOS conference in Lille,France. I am very grateful for the constructive commentsand feedback from colleagues at both of these presenta-tions as well as from two anonymous reviewers, whichhave helped shape the ideas and focus of this paper.

1KrackPlot: http://www.andrew.cmu.edu/user/krack/krackplot.shtml – well-established SNA software.2UCINET: http://www.analytictech.com/ucinet/ – well-established SNA software.3Pajek: http://pajek.imfm.si/doku.php – specialized soft-ware for dealing with large networks.4Metasight: http://www.morphix.com/Pages/MetaSight/MetaSight.html – uses email data as input.5See Freeman (2000) for an overview of the history anddiversity of social network visualization tools, and seewikipedia.org/wiki/Social_network_analysis_software(accessed 22 November 2011) for a good overview of alarge range of software applications for the visualizationof social network data and links to websites for indi-vidual applications.

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British Journal of Management, Vol. ••, ••–•• (2012)DOI: 10.1111/j.1467-8551.2012.00835.x

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and leveraged for ‘visible results’ within organiza-tions (Cross and Parker, 2004; Cross and Thomas,2009; Cross, Borgatti and Parker, 2002; Krack-hardt and Hanson, 1993). Whilst it is recognizedthat not all network research employs visualiza-tion tools to depict the social structure under inves-tigation, there are nevertheless a rapidly growingnumber of examples that can be found within theacademic literature, including the majority of thestudies referenced in this paper, as well as in prac-titioner texts and on consultancy websites.

However, despite the growing use of SNA bybusiness and management academics and practi-tioners, it is contended that too little attention inthe literature has been focused on the nature ofthe data being collected, the manner in which it isbeing displayed, or the associated ethical issuesin such studies. For example, it is common forSNA studies in the management field to be silentor underplay important issues relating toboundary-setting, informant response rates anddecisions concerning network visualization (e.g.Allen, James and Gamlen, 2007; Chiffoleau, 2005;Stephenson and Lewin, 1996). Ethical issues inrelation to SNA research are raised rarely. Theobjective of this paper then is to heighten aware-ness of these concerns within the business andmanagement community. Issues concerning indi-vidual techniques for processing and analysingsocial network data tend to be highly technicaland, as such, are considered better dealt with inthe specialist social network literature.6

In this paper we start by providing an overviewof the scope of SNA usage across the field ofbusiness and management. We then turn to anevaluation of the accuracy and completeness ofthe data in such network studies, and highlightpossible ways in which weaknesses apparent insurvey methods, for example, might be mitigated.We consider the nature of the network visualiza-tion itself, reflecting on the multiple ways in whicha network may be viewed and depicted and howsuch depictions may be interpreted. Finally, wesurface the ethical and privacy issues associatedwith network research. These are increasingly per-tinent because of the rise in use of SNA by con-sultants and managers in relation to decision-making within organizations (Cross et al., 2001;

Parker, Cross and Walsh, 2001). Indeed, Borgattiand Molina (2003, p. 338) rightly warn us that‘consideration of ethical issues [is] increasinglycritical as organizations start basing person-nel and reorganization decisions on networkanalyses’.

The breadth of SNA usage in businessand management

Over the last couple of decades there has been arapid growth in the use of SNA techniques toresearch a wide range of business and manage-ment issues and contexts. More recently, suchtechniques have been applied to the study of spe-cialist academic communities within business andmanagement itself. However, perhaps most inter-esting is its diffusion into business consultancyand business practice.

One of the earliest examples of the analysis of asocial network is associated with the classic Haw-thorne studies of the 1930s, where hand-drawn‘sociograms’ were produced to map interactionsrelated to friendship, antagonisms, controversiesand the helping of colleagues (Roethlisberger andDickson, 1939, pp. 502–507). Since then, othershave mapped, for example, the informal commu-nication networks between engineers within theR&D function of an organization (Allen, 1977, p.208; Allen, James and Gamlen, 2007, p. 186),the inter-organizational cooperation networksbetween scientists and innovators (Cantner andGraf, 2006, p. 471; Chiffoleau, 2005, pp. 1200–1202; Fleming, King and Juda, 2007, pp. 940–941),cluster formation in biotechnology (Casper, 2007,pp. 450–452), social networks and knowledgemanagement in supply chains (Capó-Vicedo,Mula and Capó, 2011; Kim et al., 2011) and theconnections between the founders of the semicon-ductor sector (Castilla et al., 2000, p. 228). Studieshave also mapped workplace friendship networks(Kilduff and Krackhardt, 1994, p. 94), gender andracial diversity in workplace support and informa-tion networks (Stephenson and Krebs, 1993, pp.70–71; Stephenson and Lewin, 1996, pp. 179–180)and friendship among the French financial elite(Kadushin, 1995, p. 211).

There are also a growing number of fascinatingSNA studies that have turned the gaze inward,onto the academic communities within businessand management, such as those mapping the

6Such as Social Networks, Sociometry, Connections, theJournal of Social Structure and the Journal of Quantita-tive Anthropology.

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© 2012 The Author(s)British Journal of Management © 2012 British Academy of Management.

‘invisible college’ among B2B marketing research-ers (Morlacchi, Wilkinson and Young, 2005, p.14), economists concerned with technology andinnovation (Verspagen and Werker, 2003, p. 408;2004, p. 1425), the information systems com-munity (Vidgen, Henneberg and Naudé, 2007),hospitality management researchers (Hu andRacherla, 2008, p. 306) and around specific jour-nals, such as R&D Management (McMillan, 2008,pp. 74–76). Broader based studies have alsosought to map the invisible college among themost prominent researchers in management andorganization studies (Acedo et al., 2006, pp. 976–977), and the interconnectedness of editorialboard membership across the Financial Times 40management and business journals (Burgess andShaw, 2010, pp. 636–640). Hu and Racherla(2008), for example, as with a number of theabove studies, employ co-authorship data fromprominent journals in the field. Whilst they recog-nize limitations to their study, such as its inabilityto capture informal interactions, they suggestworryingly that the resulting network maps could‘serve as alternative metrics to evaluate (or at leastimply) research impacts and contributions of indi-vidual researchers by research collaborations,which in many cases is difficult to detect by theconventional methods’ (Hu and Racherla, 2008,p. 311).

Over the last decade, SNA techniques have alsobeen applied increasingly in consultancy work, inorder to reveal informal structures and knowledgeflows and identify influential individuals, such asgatekeepers and opinion leaders. Cross et al.(2001) argue that SNA achieves this by enablingthe production of an ‘X-ray’ of the informalnetwork. Parker, Cross and Walsh (2001), forexample, have applied such techniques within aconsortium of Fortune 500 companies and gov-ernment agencies, often as a precursor to identi-fying ‘intervention opportunities’. In one case,involving a consulting practice, Parker, Cross andWalsh (2001, p. 27) argue that ‘the result of inter-ventions was significant . . . the group began tosell more . . . [and] a network analysis conductednine months later revealed a well-integratedgroup that was leveraging and seeking its knowl-edge much more effectively’.

This consultancy work reflects a growing rec-ognition within the areas of human resourcemanagement and organizational developmentof the potential of SNA (Bunker, Alban and

Lewicki, 2004; Hatala, 2006; Lengnick-Hall andLengnick-Hall, 2003; Stephenson and Lewin,1996). Indeed, Hatala (2006, p. 65) argues that‘SNA can provide HRD [human resource devel-opment] practitioners with valuable relationalinformation that can assist in the assessment ofperformance and implementation interventions’.However, despite their extensive research andconsulting work, Parker, Cross and Walsh (2001,p. 28) recognize that ‘network analysis is not acure-all’ and that ‘if applied without proper fore-thought, the results can be inconclusive at bestand damaging at worst’. This point is importantto reflect upon since, as Borgatti and Molina(2003, pp. 337–338) stress, ‘The stakes are higherin the practice setting than in the academicsetting, because the purpose of the networkresearch there is explicitly to make decisions thatdirectly or indirectly will affect the lives ofemployees’.

Evaluating the accuracy andcompleteness of SNA data inputsThe nature of network data

Social networks comprise three core components:actors, links, and flows. They are constructed byidentifying and then connecting individual dyads.Typically, such network data are obtainedthrough a questionnaire survey completed by themembers of the network,7 although data can alsobe collected through interviews, documents,observation and from various electronic sources.A link is considered to exist where both actors ina dyad report a relationship with the other; this istermed ‘reciprocal nomination’ (Stork and Rich-ards, 1992). However, ‘non-reciprocal nomina-tions’ may be ‘symmetrized’ (Scott, 1991), i.e. arelationship may be considered to be present evenwhen it is reported by only one of the two indi-viduals in the dyad.

Since network data cannot typically be col-lected instantaneously and may relate to an eventtaking place over a period of time (e.g. the rela-tionships mobilized during the development of a

7For a sample of social network questionnaires seethe webpage for Professor Krackhardt of the HeinzSchool of Public Policy and Management, CarnegieMellon University (www.andrew.cmu.edu/user/krack/questionnaires.shtml).

Data Inputs and Visual Outputs in Social Network Analysis 3


new product), it is subject to ‘temporal grouping’.That is, network data are aggregated from acrossthe period of data collection, effectively conflatingtime and disregarding the ordering of relationalevents. Collecting network data from blogs, news-groups, email and chat rooms is becoming morecommon and may serve to resolve some of theseissues, although such internet sources presenttheir own ‘accuracy’ and ethical issues.

It has been argued that social network studiesoften under-emphasize the flows through thenetwork, whilst over-emphasizing the quantityrather than the ‘quality’ or ‘utility’ of networkrelationships and interactions (Conway, Jonesand Steward, 2001).

Problems associated with boundary-setting andchoices concerning ‘rules of inclusion’

Where SNA is being undertaken among an iden-tifiable group of individuals, such as a projectteam or department within an organization, thenthe membership is likely to be reasonably clearto the researcher. Nevertheless, it is often theboundary-spanning relationships that are of par-ticular interest and importance to researchers andmanagers alike (Tushman, 1977), and these link-ages can be remain ‘hidden’ if the boundaryaround data collection is set too tightly to themembership of the group. However, in manycases the membership of the group of individualsunder investigation is poorly defined, such as withinformal networks and communities (Ghani,Donnelly and Garnett, 1998). In such instances,the researcher may sensibly begin by approachingthose known members and proceed by identifyingfurther members from the responses from theseknown members (Scott, 2000, p. 61). In employ-ing such a ‘snowball’ sampling approach, thenetwork researcher must at some point decidewhere and when to stop collecting data, otherwisethey will be drawn into ‘the general ever-ramifying, ever-reticulating set of linkages thatstretches within and beyond the confines of anycommunity or organisation’ (Mitchell, 1969,p. 12). Yet, in doing so, the researcher sets anominal boundary for the network and effectivelydecides who is, and therefore who is not, part ofthe network (Laumann, Marsden and Prensky,1983).

In very large bounded groups, such as acompany division of several hundred employees,

the collection of network data can very quicklybecome unmanageable. In such instances, it is notadvisable in SNA to simply select a representativesample, since this does not provide a ‘usefulsample of relations’ (Scott, 2000, p. 59). One strat-egy to cope with large networks is for theresearcher to establish their own ‘rules of inclu-sion’, which may be based on characteristics suchas the role, seniority or gender, for example, of themembers of the larger group. Such rules of inclu-sion should be clearly linked to the research ques-tions of the project (Laumann, Marsden andPrensky, 1983).

A specific example of these boundary-settingand sampling decisions can be seen in a recentinvestigation of the informal problem-solvingnetwork within ICI’s R&D function (Allen, Jamesand Gamlen, 2007). In this study, to make datacollection manageable, only senior personnel wereselected, representing 152 of approximately 400R&D staff. Furthermore, the researchers did notlook at interactions across the organizationalboundary, although they recognized that ‘externalnetworks and links with scientific communitiesare very important for research scientists’ (Allen,James and Gamlen, 2007, p. 184). As a result, theinformal problem-solving network that is identi-fied by the researchers is partial, and under-represents the complexity and diversity of theinternal and external linkages. Thus, boundary-setting and sampling decisions can have a pro-found impact on the structure of the network thatis revealed, and as a result Fombrun (1982, p. 288)warns that the conclusions drawn from a networkstudy ‘must be carefully scrutinized for the possi-bility of alternative explanations grounded in theeffects of the untapped networks’.

Problems associated with missing orinaccurate data

Having established the boundary and rules ofinclusion for a network study, it is important thatclose to a ‘complete’ data set is obtained by theresearcher. Parker, Cross and Walsh (2001, p. 28)argue that ‘while you don’t have to get 100%response, we typically shoot for at least 80%response from the group we’re analysing’. In con-trast, others contend that ‘the analysis andmapping of the structure of the network is espe-cially sensitive to missing data’ (Huisman, 2009, p.2) and that missing data can be ‘very misleading

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. . . if the most central person is not pictured . . . orif the only bridge between the groups is not shown’(Borgatti and Molina, 2003, p. 339). The latterpoint is emphasized graphically in Figures 1 and 2,which illustrate the distortion of the network struc-ture as a result of missing data (inspired by Bor-gatti and Molina, 2003, p. 340). In Figure 1, datahave been collected and mapped for all actors andrelationships in a network, whilst in Figure 2,depicting the same network, data are missing fortwo actors, i.e. actors 10 and 11, who hold impor-

tant positions in the network. As a result of thesemissing data in the latter visualization, the bridgebetween the two networks remains invisible to theresearcher. Such bridges are considered importantfor promoting novelty and creating entrepre-neurial opportunities (Burt, 1992, p. 26).

Missing and inaccurate network data can arisefrom a number of sources. Principal among theseare the non-response of network members, ques-tionnaire design, and informant bias (Kossinets,2006).

Figure 1. Arrangement 1 (data collected for all actors and relationships in the network)

Figure 2. Arrangement 2 (same network as Figure 1 but with data missing for actors 10 and 11)



Missing data arising from the non-response ofnetwork members

The problems in collecting network data are oftencompounded by the non-response of a propor-tion of network members. Questions typicallyemployed in collecting network data are ‘sensi-tive’ (Tourangeau, Rips and Rasinki, 2000, p.255), and the mapping of network data canexpose the network status of individuals. Thismay further deter individuals from being involvedin such research, especially where it is beingemployed to make managerial decisions (Hatala,2006). To a certain extent, non-responses can beameliorated through a process of ‘symmetriza-tion’ (Scott, 1991). That is, where a networkmember does not respond to a survey, it might bepossible to determine their connections wherenetwork members that do respond indicate linkswith these non-respondents. Clearly, the efficacyof such an approach diminishes as the percentageof non-response increases, although simulationsindicate that reasonable results can be achievedwith up to a 20% non-response rate (Huisman,2009). Even so, response rates below 100% havethe potential to miss crucial network linkages.

Missing data arising from questionnaire design

Questionnaires are a common tool for collectingSNA data, and thus questionnaire design alsoplays its part in the ‘completeness’ and ‘reliability’of a network data set. SNA questionnaires typi-cally incorporate only a very limited number ofquestions since these often need to be answered inrelation to a sizable group of individuals. Thequestionnaire may include the full list of names ofthe group under investigation, against whichrespondents may be asked to confirm all thoseindividuals with whom they communicate.However, this technique is not possible where themembership of the group is not clear to theresearcher. In such circumstances the question-naire may be employed to reveal the networkmembership by asking the known members toindicate the names of those with whom they com-municate. Such an approach would then employ asnowball sampling strategy. In both instances,recall by respondents of weak connections orinfrequent interactions can be an issue, and maybe compounded where the group is particularlylarge or where the full names of contacts are not

known by respondents. This is important since,whilst strong ties promote information flow, weakties provide information novelty (Burt, 1992, p.26). It is also important when designing question-naires to be wary of the terms employed. Forexample, many network studies ask respondentsto identify their friends, yet even the term ‘friends’is very ambiguous and can mean different thingsto different respondents (Fischer, 1982).

Inaccurate data arising from informant bias

Following a number of experiments to testinformant accuracy in reporting past communica-tions, Bernard et al. (1984, p. 499) concluded that‘what people say about their communicationsbears no useful resemblance to their behavior’,since respondents recalled less than 50% of theirinteractions correctly. They found that respond-ents typically make two types of recall error – theyforget some of those with whom they have inter-acted and they incorrectly recall interactions withothers with whom they have not. In addition togeneral ‘memory decay’, there are a number offactors that impact the accurate recall of interac-tions and relationships, such as their perceivedsalience by the respondent, the specificity of thebehaviour being investigated and the size of thenetwork (Bell, Belli-McQueen and Haider, 2007).Furthermore, and not surprisingly, respondentsare much better at recalling their own relation-ships (i.e. ‘direct’ or ‘first-order’ connections)than the relationships of those with whom theyare connected (i.e. ‘indirect’ or ‘second-order’connections).

Such research would seem to undermine dra-matically the utility of ‘recalled’ network data.However, subsequent research by Freeman,Romney and Freeman (1987, pp. 321–322) foundthat informants typically drew from ‘somewherebetween experience and recall’ in their responses.That is, ‘what is recalled . . . is what is typical –whether it happened or not’. Interestingly, as aresult, this research reveals that individuals are, infact, very good at recalling enduring patterns ofrelations with others, although this will lead to anunder-reporting of weak ties. The accuracy ofnetwork data may also be distorted by ‘self-presentation’ (Goffman, 1973), i.e. respondentsperhaps wanting to be viewed as more connectedor interactive than they actually are. Researchconcerning the self-presentation of individuals on

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social networking sites, for example, has foundthat users often present ‘hoped-for possible selves’online that differ from their ‘real selves’ offline(Zhao, Grasmuck and Martin, 2008).

Alternative data collection methods andalternative data sources

Although network studies are often associatedwith the collection of data via questionnaires, avariety of methods and data sources may beemployed to reveal network data. These include,for example, interviews (e.g. Cross et al., 2001;Freeman, Freeman and Michaelson, 1989), obser-vation (e.g. Conti and Doreian, 2010; Freeman,Freeman and Michaelson, 1989), biographies(Crossley, 2008), personal letters (Edwards andCrossley, 2009), co-citation data from journalarticles (e.g. Acedo et al., 2006; Hu and Racherla,2008) and social networking sites, such as Face-book (e.g. Lewis et al., 2008). Each approach hasits inherent strengths and weaknesses. Lewis et al.(2008, p. 341), for example, highlight the ease withwhich network data can be obtained from Face-book, whilst also recognizing that respondents‘differ tremendously in the extent to which they“act out their social lives” on Facebook’.

There is evidence that network researchers areincreasingly employing multiple methods in orderto yield complementary data (e.g. Conti andDoreian, 2010; Crossley, 2008; Edwards andCrossley, 2009; Human and Provan, 1997; Parkand Kluver, 2009). In this regard, quantitativeapproaches may be viewed as being relativelyeffective at revealing the structure of networks,whilst the in-depth data available through quali-tative approaches may be seen as more effective inproviding insight into the process, content andcontext of relationships and interactions. In somecases mixed methods have been employed explic-itly to triangulate the data. Lievrouw et al. (1987),for example, in their study of the intellectual con-nections between biomedical scientists, employedboth co-citation data and interviews. Neverthe-less, in recent years there has been an increasingrecognition that qualitative methods have beenunder-utilized and that there is a need for theadoption of mixed methods in network researchto broaden and deepen our understanding(Coviello, 2005; Hoang and Antoncic, 2003; Jack,2010). In particular, Coviello (2005) argues that

mixed methods have a useful role to play in col-lecting data on network dynamics.

Network visualization and earlyvisualization techniques

Academic studies have employed network visuali-zation techniques for over 75 years to reveal thesocial structure in a huge variety of interestingcontexts, from mapping the social structureamong cohorts of school pupils (Moreno, 1934,pp. 154–161), the interlocking directoratesbetween organizations (Levine, 1972, p. 15) andthe spread of AIDS through social contacts(Klovdahl, 1985, p. 1204), to revealing terroristnetworks (Krebs, 2002, pp. 46, 50), informal con-nections in Formula 1 (Henry and Pinch, 2000, p.200) and the social network of the UK ‘punk’movement (Crossley, 2008, p. 101).

Moreno (1934) is generally credited with the firstattempts to visualize social networks. His ‘socio-grams’ were hand-drawn depictions. So too werethe network visualizations of others in the subse-quent decades (e.g. Levine, 1972; Roethlisbergerand Dickson, 1939, pp. 502–507). Yet, despite thepower of the graphic for displaying relational data,network visualizations remained relatively rareuntil recently. Klovdahl (1986, p. 313) attributesthis under-utilization to ‘the time and tediuminvolved in producing hand-drawn diagrams’ and‘the impossibility of manipulating these once theyare drawn’. During this period, a ‘data matrix’ waswidely employed to record and display networkdata (Scott, 2000, p. 40). Table 1 represents thenetwork in Figure 3 as a data matrix, where a ‘1’indicates that a link exists between two actors anda ‘0’ indicates that no link exists. Interpreting suchmatrices remains a skill associated with experi-enced network researchers. Given these alterna-tives for displaying network data, it is easy to seewhy the emergence of network visualization soft-ware has been such an important innovation in thepopularization of SNA, particularly among con-sultants and practitioners.

One of the key features of network visualiza-tion software is the ease with which it allows theresearcher to manipulate the graphic, such as inthe re-positioning or removal of actors. ManySNA software packages allow for the automatedpresentation of network data using what is termed‘multi-dimensional scaling’. Scott (2000, p. 149)



notes that at its simplest multi-dimensionalscaling is a technique for converting networkmetrics, such as ‘centrality’ and ‘path distance’,into physical distance on the screen or page. Thiscan be a powerful way for revealing clusters, forexample. However, as the values of networkmetrics change, so too do the physical positions ofindividual actors on the screen or page, which canbe confusing when attempting to compare anetwork at different points in time.

Network visualizations are currently employedin a number of different ways. As an output froma network study, they can provide a powerfulmedium for displaying and revealing the key fea-tures of the network under investigation, such as‘clusters’, ‘structural holes’ and ‘bridges’. These,in turn, can inform consultants and practitionersof potential interventions to alter the morphologyof the network toward particular goals, such

as improving communication and knowledgeflow between distinct organizational groups.Network maps are sometimes used during thedata collection process itself, for example, as away of interacting with respondents to confirmthe ‘completeness’ of a network created from anearlier data collection phase. They can also beemployed to co-create the network in ‘real time’, aprocess sometimes referred to as ‘participatorymapping’ (Lubbers et al., 2010), and they havebeen employed to help guide the researchertoward fruitful areas of focus during subsequentdata collection phases (Biddex and Park, 2008;Park and Kluver, 2009). Network visualizationsalso have a role to play in aiding the process oftheory building, since through the manipulationof a depiction new insights can emerge (Conwayand Steward, 1998; Klovdahl, 1981, 1986;Moody, McFarland and Bender-deMoll, 2005).

Table 1. An example of a ‘data matrix’ – this employs the same network data as Figure 3

Helen Frances John Alan Peter Mike Jane Will Mark Abby Steve

Helen 0 1 1 1 0 0 0 0 0 0 0Frances 1 0 1 1 0 0 0 0 0 0 0John 1 1 0 1 1 1 0 0 0 0 0Alan 1 1 1 0 1 1 0 0 1 0 0Peter 0 0 1 1 0 1 0 0 0 0 0Mike 0 0 1 1 1 0 0 0 0 0 0Jane 0 0 0 0 0 0 0 1 1 1 1Will 0 0 0 0 0 0 1 0 1 1 1Mark 0 0 0 1 0 0 1 1 0 0 1Abby 0 0 0 0 0 0 1 1 0 0 0Steve 0 0 0 0 0 0 1 1 1 0 0

Figure 3. Arrangement 1 (arranged to emphasize clusters and bridge)

8 S. Conway


‘The map is not the territory’: themultiple visual representations of anetwork structureThe role of the researcher in designing thenetwork depictionIt is clear from a review of a broad array of networkdepictions in the literature, such as those indicatedearlier, that there is considerable variety in thenetwork data that are displayed and the way thatthese data are represented. This is perhaps notsurprising, given that it would appear that manynetwork visualizations are arrived at through trialand error (Bertin, 1983, p. 271; Freeman, 2000)without recourse to ‘a set of recognised conven-tions’ (Bender-deMoll and McFarland, 2006;Conway and Steward, 1998). Indeed, there is no‘one right way’ to depict a network (McGrath andBlythe, 2004; Scott, 2000, p. 65).

There are two prominent approaches to theproduction of network graphics. The first mightbe labelled a ‘graphical excellence’ approach, astypified by the work of Tufte (1983, 1990) andBertin (1983). From this orientation, ‘excellencein . . . graphics consists of complex ideas commu-nicated with clarity, precision, and efficiency’(Tufte, 1983, p. 13). This is achieved through theconsidered use of what Bertin (1983, p. 71) hastermed the ‘visual’ or ‘retinal’ variables, such assize, colour and shape, in depicting the individualactors, links and flows. The second may be termed

a ‘visual argument’ approach (Simon, 1969, p. 5).From such a standpoint, Levine (1972, p. 14)argues that ‘the value (or deceptiveness) of a[graphical] representation lies in what it suggests. . . its ability to stimulate thought’. Whilst thesetwo approaches are potentially complementary,for Tufte (1983, p. 51) ‘graphical excellence’requires the researcher to ‘tell the truth about thedata’ via the visual display; clearly this is at oddswith a perspective that seeks to emphasize a par-ticular version of the ‘truth’.

There is often a trade-off between ‘seeing theoverall forest – the clusters of overall groups andtheir relative social proximity or ordering in rela-tion to each other – and seeing the finer detail ofthe trees – identifying key players and roles withinthese groups’ (McGrath, Krackhardt and Blythe,2003, p. 46). Through the graphic, the researchermay seek to highlight particular features of thenetwork, such as clusters of actors, bridgesbetween clusters, or the diversity and size of theoverall network (Bender-deMoll and McFarland,2006; Conway and Steward, 1998; McGrath andBlythe, 2004). However, different spatial arrange-ments of the same network might either highlightor obscure such network features. This point isemphasized in Figures 3 and 4 (inspired byMcGrath, Blythe and Krackhardt, 1997, p. 226).

Consideration must also be given to the selec-tion of the characteristics of the actors, links andflows to be displayed. A network map is able to

Figure 4. Arrangement 2 (circle arrangement)



incorporate a variety of quantitative and qualita-tive information. However, the choice of visualvariable to be employed in the display of suchdifferent types of data is crucial (Bertin, 1983, p.71). Typically, ‘size’ is most effectively mobilizedfor the ‘quantitative’ features of actors and links,such as years of experience or the strength of arelationship. In contrast, ‘colour’ and ‘shape’ arebest suited to displaying ‘qualitative’ features,such as an actor’s gender or functional location,and the type of ‘flow’ through a link (e.g. knowl-edge, friendship, power).

The viewer’s interpretation of a network depiction

There are a number of dangers in the choicesmade by researchers to encode certain features ofthe actors, links or flows, or in the manipulationof the network graphic in order to present a spe-cific ‘visual argument’. First, there is the possibil-ity, whether intentional or accidental, that theviewer might be misled about certain characteris-tics of the network (Bender-deMoll and McFar-land, 2006). Second, relatively little is knownabout how viewers interpret or decode thenetwork visualizations they are presented with(Bender-deMoll and McFarland, 2006; McGrathand Blythe, 2004; McGrath, Krackhardt andBlythe, 2003). In part, this is because viewers‘bring a rich vocabulary of graphical idioms andconventions to the table when they interpret thevisualization’ (McGrath and Blythe, 2004, p. 1).

From researcher generated aggregated networkmaps to individualized ‘cognitive maps’

It is the norm for network analysts to aggregatethe network data of individual respondents tocreate a single network map. Yet there has longbeen evidence to indicate that individuals within anetwork may have very different ‘cognitive maps’or ‘cognitive structures’ of the very same network(Krackhardt, 1987, 1990). That is, ‘to someextent, social structure is in the eye of thebeholder’ (Kilduff and Krackhardt, 1994, p. 87).Colville and Pye (2010, p. 378) contend that ‘thisposes problems of aggregation . . . as you raise thelevel of the analysis from the individual to thecollective in search of network insight’. Interest-ingly, a recent study by Kilduff et al. (2008)revealed that individuals perceive more clusteringthan is present in the ‘actual’ network and attrib-

uted more popularity and brokerage to individu-als they perceived as popular. As a result, Kilduffet al. (2008, p. 25) argue that:

Perceiving the organization as a small world mayreassure the individual concerning the approach-ability of even distant people . . . . On the otherhand, a tendency to misperceive clustering . . .together with a tendency to attribute more impor-tance to perceivedly popular people, may lead activenetworkers to be overly confident in picking keypeople in the network with whom to form attach-ments. Managers, for example, might assume thatthey are keeping in touch with all the importantclusters, when, in fact, the clustering and connectiv-ity they perceive are more figments of their imagi-nation than accurate features of the social network.

That individuals perceive such differentnetwork structures may be seen as a process of‘sense-making’ by individuals within an organiza-tion when they are faced with complex anddynamic webs of relationships (Colville and Pye,2010; Purchase, Lowe and Ellis, 2010; Ramos andFord, 2011; Weick, 1995, pp. 38–39). Thus, wemight expect the cognitive map of an individual toshape their behaviour within a network ratherthan an aggregated version of the network map asrepresented by the researcher.

Emerging approaches to depictingnetworks – moving from ‘snapshots’to ‘movies’

Social networks are typically dynamic structures.However, attempts to ‘capture’ and ‘make visible’networks have often led to the mapping of a single‘snapshot in time’ of the network structure. Indoing so, there is a danger that the network visu-alization presents an ossified version of thenetwork. This is likely to reinforce the prevailingattention on ‘static structures’ rather than ‘thedynamic processes that transform those matricesof transactions in some fashion’ (Emirbayer,1997, p. 305). Attempts to address this concernhave led to a growing interest in longitudinalresearch in the study of social networks. Interest-ing examples include that of social network for-mation and inter-firm mobility within the SanDiego biotechnology cluster (Casper, 2007), fieldevolution in the life sciences (Powell et al., 2005)and changes in managerial sense-making (Öberg,Henneberg and Mouzas, 2007).

10 S. Conway


In longitudinal research, data are typicallycollected at intervals and displayed as a seriesof snapshots (e.g. Casper, 2007; Degenne andLebeaux, 2005; Powell et al., 2005). Yet forMoody, McFarland and Bender-deMoll (2005, p.1207), such depictions ‘do a poor job of represent-ing change in networks’, since whilst, on the onehand, longitudinal data might capture the endur-ing patterns within a network, on the other, thefluctuations in relationships and interactionsbetween the sampling periods are lost. Further-more, each snapshot suffers from the ‘temporalgrouping’ noted earlier. Increasing the frequencyof these discrete waves of data collection and theresulting number of snapshots can help mitigatethese concerns, although this is likely to have amajor impact on the effort required to collect therequisite data. Nevertheless, Bender-deMoll andMcFarland (2006) argue that whilst we can ‘talkusefully about network change [in such research]. . . it is difficult to argue that “dynamics” and“evolution” have been recorded’.

For researchers to effectively capture thedynamics of a network, they will need to ‘teaseapart’ the relationship between the micro-levelinteractions and the overall network. Ideally, thiswould be done by capturing changes or activity asit occurs, to collect a continuous ‘stream’ of data.These data could then be displayed not as a seriesof discrete network pictures but as an animated‘network movie’, with gradual changes in indi-vidual actors, links and flows that seamlessly andgradually reshape the network map (Bender-deMoll and McFarland, 2006; Moody, McFar-land and Bender-deMoll, 2005). New sources ofdata, particularly those associated with onlineinteractions, and innovations in data collectiontools are presenting new opportunities to achievethis challenge (Ackland, 2009; Szell and Thurner,2010). However, others have argued that a moreprocessual orientation to network studies isrequired (Purchase, Lowe and Ellis, 2010).

Issues of privacy and ethics

Despite the personal nature of much of the datacollected and presented in the typical networkstudy, surprisingly little attention has beendirected towards addressing the associated issuesof privacy and research ethics. Indeed, Breiger(2005, pp. 89–90) pulls no punches in contending

that the social network field has ‘a greater ability toarrive at incisive analyses than to comprehend theconditions for responsible uses of such analyses’.This is clearly problematic, since as Borgatti andMolina (2003, p. 337) argue, ‘In addition to all theusual ethical problems that can arise with any kindof inquiry, network analyses, by their very nature,introduce special ethical problems that should berecognized’. For example, in order to construct anetwork, the researcher must be able to identify therespondent and the individuals to whom therespondents say they are linked. Thus, althoughanonymity may be provided at the data presenta-tion stage (i.e. within the network graphic), it is notpossible during the data collection stage. Further-more, network visualizations are ‘low-level dis-plays that represent the raw data’ rather than‘highly digested outputs of analysis’ (Borgatti andMolina, 2003, p. 341), and thus, where they areemployed, it is often possible for knowledgeableindividuals to identify others within the networkeven where they have been anonymized.

It is common for network studies to ask personalquestions, such as ‘Who are you friends with atwork?’ or ‘Who do you socialise with outside ofwork?’ However, despite the use of consent forms,most respondents in network studies will not havebeen involved in such research before and areunlikely to be aware of how they might feel if theyare identified through such questions as being‘marginal’ or ‘unliked’ in their group. Further-more, where the research forms part of a consul-tancy project, rather than a piece of academicwork, respondents may also be unaware of thepossible consequences that might result from sub-sequent management interventions intended toaddress features revealed by the network study. AsBorgatti and Molina (2003, p. 344) state, ‘If sub-ordinates do not understand that their answers onthe survey could determine their fate, this could beseen as deceptive and constitute an unethical use ofnetwork analysis’.

Interestingly, consent can also be a majorproblem in SNA with regard to non-participants.Since respondents in SNA research reveal detailsabout their relationships and exchanges withothers, the non-participation of an individual in astudy does not rule out the possibility that datamay be collected about them or that they may beincluded in subsequent analyses or network depic-tions. There is also increasing use by academics,consultants and managers of electronic sources of



‘social’ data from social networking sites, chatrooms, blogs and email logs, for example. Theprivacy and consent issues relating to such datasources have received insufficient serious atten-tion. Hoser and Nitschke (2010) contend that it isnot enough to assume the free use of social datasimply because it resides in the public domain,arguing for the establishment of a code of behav-iour that embraces the notion of ‘perceivedprivacy’ (Eyenbach and Till, 2001); thus dataposted on a social networking site or newsgroupshould only be used ‘in the context and by theaudience he or she intended it for’.

Implications for network researchersand practitioners

For Borgatti and Molina (2003, p. 337), ‘theconcept of network has become the metaphor forunderstanding organizations’, among both aca-demics and management consultants. It is withinthis context that we have sought to provide acritique of the robustness of an increasinglypopular approach for revealing and mappingsocial network structure. This critique is notintended to dismiss the potential of SNA fortheory-building or management practice, butrather to surface issues that require considerationand, where possible, resolution or mitigation.

Implications for network researchers andfurther research

We have argued that the seductive nature ofnetwork visualizations has distracted attentionaway from a number of emerging and long-standing issues in SNA. We contend that networkresearchers need to reflect more on the choicesmade concerning boundary-setting and data col-lection techniques, as well as on the potentialimpact of missing or inaccurate data. After all, asRogers (1987, p. 298) has noted, ‘without gooddata, network analysis is worthless’. Indeed, thereis a pressing need for further research to improveour understanding of the ‘patterns and conse-quences’ of missing network data since, as Kossi-nets (2006, p. 248) argues, ‘Although missing datais abundant in empirical [network] studies, littleresearch has been conducted on the possibleeffects of missing links or nodes on the measur-able properties of networks’.

McGrath, Krackhardt and Blythe (2003, p. 46)also raise concerns about our understanding of tothe way in which network visualizations are inter-preted by users, arguing ‘To be sure, we can makemore programs that seem to us as researchers/programmers to make “better” pictures; but weare relatively ignorant of how general human per-ception interacts with these fancy new features. . .’. Thus, further research is required in relationto understanding how various users of networkmaps interpret the visualizations with which theyare presented.

It was noted earlier that network studies typi-cally under-emphasize the flows through anetwork and over-emphasize the quantity ratherthan the ‘quality’ or ‘utility’ of network relation-ships and interactions (Conway, Jones andSteward, 2001). Such a pattern is likely to bereinforced by the use of network surveys or data-mining of social media logs. It is thus recom-mended that researchers adopt a mixed methodapproach, incorporating both quantitative andqualitative data collection methods.

Implications for consultants and businesspractitioners

Network researchers typically construct a singlemap. Yet, as we have indicated, research has high-lighted that individual perceptions (i.e. ‘cognitivemaps’) of a social network can vary greatly fromsuch unified visualizations. This conflation canhave far-reaching impacts on the organizationsince, as Kilduff et al. (2008, pp. 25–26) contend,such ‘schema use by individuals in their percep-tions of social worlds may affect individuals andlarger entities . . . [thus] there may be unantici-pated consequences not just for the individualsconcerned, but also for the collectivity to whichthey belong’. Consideration might be given toanalysing both the ‘cognitive maps’ of individualnetwork members and the ‘aggregated’ networkmaps produced by network analysts. Social net-works are also dynamic in nature; their structureis often fleeting and transitory. Thus, in attempt-ing to make ‘invisible’ social structures ‘visible’,network visualizations typically focus attentionon the network ‘as was’ (i.e. when the data werecollected) rather than ‘as is’. Practitioners must beaware of the implications of the time-lag betweendata collection and managerial intervention.

12 S. Conway


Practitioners must also recognize that as morenetwork audits are undertaken within theirorganization it is likely that employees might startto refuse to cooperate, or to complete surveys‘strategically’, leading to ‘a kind of dialecticalarms race’ where researchers utilize increasinglysophisticated and passive methods of data collec-tion and employees respond in kind via collusionand manipulation of the data (Borgatti andMolina, 2003, p. 345). Openness with employeesin relation to the collection and use of networkdata within organizations might help to preventthis cycle occurring.

Despite the range of ethical concerns outlinedabove, Borgatti and Molina (2003, p. 342) arguethat what ultimately matters is ‘who sees the dataand what the data will be used for’. Thus, wherethe data remain anonymized and do not result inpotential consequences for respondents, theethical ‘exposure’ may be seen to be greatlyreduced. However, these conditions are unlikelyto be met where the purpose of the study is toidentify appropriate managerial interventions toimprove organizational or individual ‘perform-ance’. It is also worth researchers seriously con-sidering whether personal questions associatedwith friendship, within both the work and non-work environments, are appropriate questions toask when the study has been commissioned bymanagers of an organization.

Interestingly, for Kadushin (2005, pp. 139, 151)the question of ‘who benefits’ is crucial, arguingthat ‘academic researchers always benefit, organi-zations, society and science may benefit, but indi-vidual respondents rarely do’. The implicationof this position is that as network researcherswe must become much more sensitized to therange of potential repercussions for respondents.In addressing this issue, some have focused onproviding a number of concrete suggestions forthe further development of research guidelinesand processes (Borgatti and Molina, 2005; Klov-dahl, 2005). However, Goolsby (2005) is bolder,contending that there is a need for developing ‘anethical imagination’ to tackle these prevailingconcerns.

References

Acedo, F., C. Barroso, C. Casanueva and J. Galán (2006).‘Co-authorship in management and organizational studies:

an empirical and network analysis’, Journal of ManagementStudies, 43, pp. 957–983.

Ackland, R. (2009). ‘Social network services as data sources andplatforms for e-researching social networks’, Social ScienceComputer Review, 27, pp. 481–492.

Ahuja, M. and K. Carley (1999). ‘Network structure in virtualorganizations’, Organization Science, 10, pp. 741–757.

Allen, J., A. James and P. Gamlen (2007). ‘Formal versus infor-mal knowledge networks in R&D: a case study using socialnetwork analysis’, R&D Management, 37, pp. 179–196.

Allen, T. (1977). Managing the Flow of Technology: TechnologyTransfer and the Dissemination of Technological Informationwithin the R&D Organization. Cambridge, MA: MIT Press.

Bell, D., B. Belli-McQueen and A. Haider (2007). ‘Partnernaming and forgetting: recall of network member’, SocialNetworks, 29, pp. 279–299.

Bender-deMoll, S. and D. McFarland (2006). ‘The art andscience of dynamic network visualization’, Journal of SocialStructure, 7. Available online at: http://www.cmu.edu/joss/content/articles/volume7/deMollMcFarland.

Bernard, H., P. Killworth, D. Kronenfeld and L. Sailor (1984).‘The problem of informant accuracy: the validity of retro-spective data’, Annual Review of Anthropology, 13, pp. 495–517.

Bertin, J. (1983). Semiology of Graphics: Diagrams, Networks,Maps, translated by W. Berg. Madison, WI: University ofWisconsin Press.

Biddex, P. and W. Park (2008). ‘Online networks of studentprotest: the case of the living wage campaign’, New Media andSociety, 10, pp. 871–891.

Borgatti, S. and P. Foster (2003). ‘The network paradigm inorganizational research: a review and a typology’, Journal ofManagement, 29, pp. 991–1013.

Borgatti, S. and J. Molina (2003). ‘Ethical and strategic issues inorganizational social network analysis’, Journal of AppliedBehavioral Science, 39, pp. 337–349.

Borgatti, S. and J. Molina (2005). ‘Toward ethical guidelines fornetwork research in organizations’, Social Networks, 27, pp.107–117.

Breiger, R. (2005). ‘Introduction to special issues: ethical dilem-mas in social network research’, Social Networks, 27, pp.89–93.

Bunker, B., B. Alban and R. Lewicki (2004). ‘Ideas in currencyand OD practice: has the well gone dry?’ Journal of AppliedBehavioral Science, 40, pp. 403–422.

Burgess, T. and N. Shaw (2010). ‘Editorial board membershipof management and business journals: a social network analy-sis study of the Financial Times 40’, British Journal of Man-agement, 21, pp. 627–648.

Burt, R. (1992). Structural Holes: The Social Structure of Com-petition. Cambridge, MA: Harvard University Press.

Cantner, U. and H. Graf (2006). ‘The network of innovators inJena: an application of social network analysis’, ResearchPolicy, 35, pp. 463–480.

Capó-Vicedo, J., J. Mula and J. Capó (2011). ‘A social network-based organizational model for improving knowledge man-agement in supply chains’, Supply Chain Management: AnInternational Journal, 16, pp. 284–293.

Casper, S. (2007). ‘How do technology clusters emerge andbecome sustainable? Social network formation and inter-firmmobility within the San Diego biotechnology cluster’,Research Policy, 36, pp. 438–455.



Castilla, E., H. Hwang, E. Granovetter and M. Granovetter(2000). ‘Social networks in Silicon Valley’. In C. Lee, W.Miller, M. Hancock and H. Rowen (eds), The Silicon ValleyEdge, pp. 218–247. Palo Alto, CA: Stanford UniversityPress.

Cattani, G. and S. Ferriani (2008). ‘A core/periphery perspec-tive on individual creative performance: social networks andcinematic achievements in the Hollywood film industry’,Organization Science, 19, pp. 824–844.

Chiffoleau, Y. (2005). ‘Learning about innovation through net-works: the development of environment-friendly viticulture’,Technovation, 25, pp. 1193–1204.

Colville, I. and A. Pye (2010). ‘Sensemaking perspective onnetwork pictures’, Industrial Marketing Management, 39, pp.372–380.

Conti, N. and P. Doreian (2010). ‘Social network engineeringand race in a police academy: a longitudinal analysis’, SocialNetworks, 32, pp. 30–43.

Conway, S. and F. Steward (1998). ‘Mapping innovation net-works’, International Journal of Innovation Management, 2,pp. 223–254.

Conway, S., O. Jones and F. Steward (2001). ‘Progress, prob-lems and prospects for network research’, Social Networks, 9,pp. 285–310.

Coviello, N. (2005). ‘Integrating qualitative and quantitativetechniques in network analysis’, Qualitative Market Research:An International Journal, 8, pp. 39–60.

Cross, R. and A. Parker (2004). The Hidden Power of SocialNetworks: Understanding How Work Really Gets Done inOrganizations. Cambridge, MA: Harvard Business Press.

Cross, R. and R. Thomas (2009). Driving Results Through SocialNetworks: How Top Organizations Leverage Networks forPerformance and Growth. San Francisco, CA: Jossey-Bass.

Cross, R., S. Borgatti and A. Parker (2002). ‘Making invisiblework visible’, California Management Review, 44, pp. 25–46.

Cross, R., A. Parker, L. Prusak and S. Borgatti (2001).‘Knowing what we know: supporting knowledge creation andsharing in social networks’, Organizational Dynamics, 30, pp.100–120.

Crossley, N. (2008). ‘Pretty connected: the social network of theearly UK punk movement’, Theory, Culture, and Society, 25,pp. 89–116.

Degenne, A. and M. Lebeaux (2005). ‘The dynamics of personalnetworks at the time of entry into adult life’, Social Networks,27, pp. 337–358.

Edwards, G. and N. Crossley (2009). ‘Measures and meanings:exploring the ego-net of Helen Kirkpatrick Watts, militantsuffragette’, Methodological Innovations On-Line, 4, pp.37–61.

Emirbayer, M. (1997). ‘Manifesto for a relational sociology’,American Journal of Sociology, 103, pp. 281–317.

Eyenbach, G. and J. Till (2001). ‘Ethical issues in qualitativeresearch on internet communities’, British Medical Journal,323, pp. 1103–1105.

Fischer, C. (1982). ‘What do we mean by “friend”? An inductivestudy’, Social Networks, 3, pp. 287–306.

Fleming, L., C. King and A. Juda (2007). ‘Small worlds andregional innovation’, Organization Science, 18, pp. 938–954.

Fombrun, C. (1982). ‘Strategies for network research in organi-sations’, Academy of Management Review, 7, pp. 280–291.

Freeman, L. (2000). ‘Visualizing social networks’, Journal ofSocial Structure, 1. Available online at: http://www.cmu.edu/joss/content/articles/volume1/Freeman.html.

Freeman, L., S. Freeman and A. Michaelson (1989). ‘Howhumans see social groups: a test of the Sailer–Gaulin models’,Journal of Quantitative Anthropology, 1, pp. 229–238.

Freeman, L., A. Romney and S. Freeman (1987). ‘Cognitivestructures and informant accuracy’, American Anthropologist,89, pp. 310–325.

Ghani, A., C. Donnelly and G. Garnett (1998). ‘Sampling biasesand missing data in explorations of sexual partner networksfor the spread of sexually transmitted diseases’, Statistics inMedicine, 17, pp. 2079>–2097.

Goffman, E. (1973). The Presentation of Self in Everyday Life.New York: Overlook Press.

Goolsby, R. (2005). ‘Ethics and defense agency funding: someconsiderations’, Social Networks, 27, pp. 95–106.

Hatala, J. (2006). ‘Social network analysis in human resourcedevelopment: a new methodology’, Human Resource Devel-opment Review, 5, pp. 45–71.

Henry, N. and S. Pinch (2000). ‘Spatialising knowledge: placingthe knowledge community of Motor Sport Valley’, Geoforum,31, pp. 191–208.

Hoang, H. and B. Antoncic (2003). ‘Network-based research inentrepreneurship: a critical review’, Journal of Business Ven-turing, 18, pp. 165–187.

Hoser, B. and T. Nitschke (2010). ‘Questions on ethics forresearch in the virtually connected world’, Social Networks,32, pp. 180–186.

Hu, C. and P. Racherla (2008). ‘Visual representation of knowl-edge networks: a social network analysis of hospitalityresearch domain’, International Journal of Hospitality Man-agement, 27, pp. 302–312.

Huisman, M. (2009). ‘Imputation of missing network data:some simple procedures’, Journal of Social Structure, 10, pp.1–29. Available online at: http://www.cmu.edu/joss/content/articles/volume10/huisman.pdf.

Human, S. and K. Provan (1997). ‘An emergent theory of struc-ture and outcomes in small-firm strategic manufacturing net-works’, Academy of Management Journal, 40, pp. 368–403.

Jack, S. (2010). ‘Approaches to studying networks: implicationsand outcomes’, Journal of Business Venturing, 25, pp. 120–137.

Kadushin, C. (1995). ‘Friendship among the French financialelite’, American Sociological Review, 60, pp. 202–221.

Kadushin, C. (2005). ‘Who benefits from network analysis:ethics of social network research’, Social Networks, 27, pp.139–153.

Kijkuit, B. and J. van den Ende (2010). ‘With a little help fromour colleagues: a longitudinal study of social networks forinnovation’, Organization Studies, 31, pp. 451–479.

Kilduff, M. and D. Krackhardt (1994). ‘Bringing the individualback in: a structural analysis of the internal market for repu-tation in organizations’, Academy of Management Journal,37, pp. 87–108.

Kilduff, M., C. Crossland, W. Tsai and D. Krackhardt (2008).‘Organizational network perceptions versus reality: a smallworld after all?’ Organizational Behaviour and Human Deci-sion Processes, 107, pp. 15–28.

Kim, Y., T. Choi, T. Yan and K. Dooley (2011). ‘Structuralinvestigation of supply networks: a social network analysisapproach’, Journal of Operations Management, 29, pp. 194–211.

14 S. Conway


Klovdahl, A. (1981). ‘A note on images of networks’, SocialNetworks, 3, pp. 197–214.

Klovdahl, A. (1985). ‘Social networks and the spread of infec-tious diseases: the AIDS example’, Social Science and Medi-cine, 21, pp. 1203–1216.

Klovdahl, A. (1986). ‘View_Net: a new tool for network analy-sis’, Social Networks, 8, pp. 313–342.

Klovdahl, A. (2005). ‘Social network research and human sub-jects protection: towards more effective infectious diseasecontrol’, Social Networks, 27, pp. 119–137.

Kossinets, G. (2006). ‘Effects of missing data in social network’,Social Networks, 28, pp. 247–268.

Krackhardt, D. (1987). ‘Cognitive social structures’, Social Net-works, 9, pp. 109–134.

Krackhardt, D. (1990). ‘Assessing the political landscape: struc-ture, cognition, and power in organizations’, AdministrativeScience Quarterly, 35, pp. 342–369.

Krackhardt, D. and J. Hanson (1993). ‘Informal networks: thecompany behind the chart’, Harvard Business Review, 71, pp.104–111.

Krebs, V. (2002). ‘Mapping networks of terrorist cells’, Connec-tions, 24, pp. 43–52.

Laumann, E., P. Marsden and D. Prensky (1983). ‘Theboundary specification problem in network analysis’. InR. Burt and M. Minor (eds), Applied Network Analysis: AMethodological Introduction, pp. 18–34. Beverly Hills, CA:Sage.

Lengnick-Hall, M. and A. Lengnick-Hall (2003). ‘HR’s role inbuilding relationship networks’, Academy of ManagementExecutive, 17, pp. 53–63.

Levine, J. (1972). ‘The sphere of influence’, American Sociologi-cal Review, 37, pp. 14–27.

Lewis, K., J. Kaufman, M. Gonzalez, A. Wimmer and N.Christakis (2008). ‘Tastes, ties, and time: a new social networkdataset using Facebook.com’, Social Networks, 30, pp. 330–342.

Lievrouw, L., R. Rogers, C. Lowe and E. Nadel (1987). ‘Trian-gulation as a research strategy for identifying invisible col-leges among biomedical scientists’, Social Networks, 9, pp.217–248.

Lubbers, M., J. Molina, J. Lerner, U. Brandes, J. Avila and C.McCarty (2010). ‘Longitudinal analysis of personal networks.The case of Argentinean migrants in Spain’, Social Networks,32, pp. 91–104.

McGrath, C. and J. Blythe (2004).‘Do you see what I want youto see? The effects of motion and spatial layout on viewers’perceptions of graph structure’, Journal of Social Structure,5. Available online at: http://www.cmu.edu/joss/content/articles/volume5/McGrathBlythe/.

McGrath, C., J. Blythe and D. Krackhardt (1997). ‘The effect ofspatial arrangement on judgments and errors in interpretinggraphs’, Social Networks, 19, pp. 223–242.

McGrath, C., D. Krackhardt and J. Blythe (2003). ‘Visualizingcomplexity in networks: seeing both the forest and the trees’,Connections, 25, pp. 37–47.

McMillan, G. (2008). ‘Mapping the invisible colleges of R&DManagement’, R&D Management, 38, pp. 69–83.

Mitchell, J. (1969). Social Networks in Urban Situations. Man-chester: Manchester University Press.

Moody, J., D. McFarland and S. Bender-deMoll (2005).‘Dynamic network visualization’, American Journal of Soci-ology, 110, pp. 1206–1241.

Moreno, J. (1934). Who Shall Survive?: A New Approach to theProblem of Human Interrelations, Nervous and Mental DiseaseMonograph Series No. 58. Washington, DC: Nervous andMental Disease Publishing.

Morlacchi, P., I. Wilkinson and L. Young (2005). ‘Social net-works of researchers in B2B marketing: a case study of theIMP Group 1984–1999’, Journal of Business-to-Business Mar-keting, 12, pp. 3–34.

Öberg, C., S. Henneberg and S. Mouzas (2007). ‘Changingnetwork pictures: evidence from mergers and acquisitions’,Industrial Marketing Management, 36, pp. 926–940.

Park, H. and R. Kluver (2009). ‘Trends in online networkingamong South Korean politicians – a mixed-methodapproach’, Government Information Quarterly, 26, pp. 505–515.

Parker, A., R. Cross and D. Walsh (2001). ‘Improving collabo-ration with social network analysis’, Knowledge ManagementReview, 4, pp. 24–28.

Powell, W., D. White, K. Koput and J. Owen-Smith (2005).‘Network dynamics and field evolution: the growth of inter-organizational collaboration in the life sciences’, AmericanJournal of Sociology, 110, pp. 1132–1205.

Purchase, S., S. Lowe and N. Ellis (2010). ‘From “taking”network pictures to “making” network pictures: a newmetaphorical manifesto for industrial marketing research’,Journal of Organizational Change Management, 23, pp. 595–615.

Ramos, C. and I. Ford (2011). ‘Network pictures as a researchdevice: developing a tool to capture actors’ perceptions inorganizational networks’, Industrial Marketing Management,40, pp. 447–464.

Roethlisberger, F. and W. Dickson (1939). Management and theWorker. Cambridge, MA: Harvard University Press.

Rogers, E. (1987). ‘Progress, problems and prospects fornetwork research’, Social Networks, 9, pp. 285–310.

Scott, J. (1991). Social Network Analysis: A Handbook. London:Sage.

Scott, J. (2000). Social Network Analysis: A Handbook, 2nd edn.London: Sage.

Simon, H. (1969). The Sciences of the Artificial. Cambridge,MA: MIT Press.

Stephenson, K. and V. Krebs (1993). ‘A more accurate way tomeasure diversity’, Personnel Journal, 72, pp. 66–74.

Stephenson, K. and D. Lewin (1996). ‘Managing workforcediversity: macro and micro level HR implications of networkanalysis’, International Journal of Manpower, 17, pp. 168–196.

Stork, D. and W. Richards (1992). ‘Nonrespondents in commu-nication network studies: problems and possibilities’, Groupand Organization Management, 17, pp. 193–209.

Szell, M. and S. Thurner (2010). ‘Measuring social dynamics ina massive multiplayer online game’, Social Networks, 32, pp.313–329.

Tourangeau, R., L. Rips and K. Rasinki (2000). The Psychologyof Survey Response. Cambridge: Cambridge UniversityPress.

Tufte, E. (1983). The Visual Display of Quantitative Information.Cheshire, CT: Graphic Press.

Tufte, E. (1990). Envisioning Information. Cheshire, CT:Graphic Press.

Tushman, M. (1977). ‘Special boundary roles in the innovationprocess’, Administrative Science Quarterly, 22, pp. 587–605.



Verspagen, B. and C. Werker (2003). ‘The invisible college ofthe economics of innovation and technology’, Estudios deEconomía Aplicada, 21, pp. 393–419.

Verspagen, B. and C. Werker (2004). ‘Keith Pavitt and theinvisible college of the economics of technology and innova-tion’, Research Policy, 33, pp. 1419–1431.

Vidgen, R., S. Henneberg and P. Naudé (2007). ‘What sort ofcommunity is the European Conference on Information

Systems? A social network analysis 1993–2005’, EuropeanJournal of Information Systems, 16, pp. 5–19.

Weick, K. (1995). Sensemaking in Organizations. London: Sage.Zhao, S., S. Grasmuck and J. Martin (2008). ‘Identify construc-

tion on Facebook: digital empowerment in anchored relation-ships’, Computers in Human Behaviour, 24, pp. 1816–1836.

Steve Conway is currently a Senior Lecturer at the University of Bath School of Management. Hisresearch interests are centred around the nature and role of social and organization networks ininnovation, entrepreneurship and knowledge creation and sharing, with a particular focus on infor-mality and boundary-spanning. He has also had a long interest in the graphical representation ofnetworks.

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Documents

A Cautionary Note on Data Inputs and Visual Outputs in Social Network Analysis