Social Network Research

  • Janet C. Long
  • Simon Bishop
Living reference work entry


Analysis of networks is increasingly seen as important for understanding the patterns, processes, and consequences of social relationships in healthcare. Networks can be formal, mandated structures (e.g., a clinical network), can emerge from sharing a common passion, or can be from routine exchanges such as referrals. Braithwaite and colleagues (2009) call for the fostering of naturally emerging networks suggesting these underpin the delivery of healthcare and play an important role in driving quality and safety. Social network analysis (SNA) emphasizes patterns of relationships and interactions between network members (actors) rather than individual attributes/behaviors or abstract social structures. SNA conceptualizes networks as composed of nodes (the actors in the group) and ties (the relationship between the actors). Ties form the structure of the network, and the nodes occupy positions within that structure. This proves a basis to investigate a wide range of issues, including communication pathways between actors (including gaps, bottlenecks, or opportunities to increase connectivity), the presence of “tribes” or silos, key players, networks of social support, and patterns of social influences on behaviors. This also allows researchers to investigate relationships between network structures (e.g., communication flows) and important outcomes (e.g., rapid dissemination of ideas). In this chapter, we will introduce readers to key debates, concepts, methods, and applications of SNA, drawing on the authors’ own studies and the growing body of healthcare literature adopting this approach. This demonstrates the contribution of SNA to understanding different types of networks, including at the individual, group, and organizational level.


Interprofessional relationships Collaboration Connectivity Brokerage Knowledge exchange 

1 Introduction

Analysis of networks is increasingly seen as important for understanding the patterns, processes, and consequences of collaborative relationships in healthcare. Networks can give a more holistic picture of the complex interactions which define the health system. Networks can be formal, mandated structures (e.g., a clinical network Haines et al. 2012), can emerge from sharing a common passion (e.g., a special interest group or community of practice Wenger et al. 2002), or can be from routine exchanges (e.g., referrals Fuller et al. 2007). Braithwaite et al. (2009) call for the fostering of naturally emerging, bottom-up networks, suggesting these underpin the delivery of healthcare and play an important role in driving quality and safety.

A network is any group of people or objects that can be said to interact or have some kind of relationship between them. Network theory provides a powerful lens through which to understand how the elements within such a group are organized, following a set of principles. The study of networks led to the realization that there are similarities between very diverse types of networks such as the neural networks of nematodes (Morita et al. 2001), power grids (Nasiruzzaman 2013), and the Internet (Carmi et al. 2007). In the social sciences, network theory is used to explain interpersonal relationships at various scales: from whole of communities (Putnam 1995) to a few clinicians exchanging information about a patient (Benham-Hutchins and Effken 2010). It provides insight into such phenomena as the influence of opinion leaders, why some companies have a competitive edge, and how effective teams work.

This chapter starts with a brief history of social network studies, followed by an introduction to basic network concepts and methods. We then describe studies which have used social network methodology to study aspects of health service delivery.

2 Network Analysis in the Social Sciences: A Brief History

The study of patterns of social relationships has been an enduring aspect of social science (Durkheim 1895; Simmel 1950). Here, we focus on social network analysis (SNA) as a distinct methodology, emerging in the mid-1930s in the social and behavioral sciences and advancing slowly but constantly over the next 60 years by a small core of researchers at Harvard. As Wasserman et al. (2005, p. 1) put it: “It was easy to trace the evolution of network theories and ideas from professors to student, from one generation to the next.”

The psychiatrist, Jacob Levy Moreno (1889–1974), is often cited as the father of network analysis although Freeman (1989) argues that the structure of networks was recognized long before this in the kinship structures such as descendant lists in the Old Testament (e.g., Genesis 5). The first use of the term “network” as it is understood today (Freeman 2004, p.35) was in Moreno’s seminal study on Hudson School for Girls and Sing-Sing Prison (Moreno and Jennings 1934). Moreno stated that the schoolgirls’ action of running away was influenced more by their position within their social network than with a conscious, independent decision. Moreno used the term “sociometry” to describe “the mathematical study of psychological properties of populations … methods which inquire into the evolution and organisation of groups and the position of individuals within them” (p.10). In other words, it is a method for eliciting and mapping the subjective feelings of individuals toward each other (Borgatti et al. 2009), focusing analytic attention on patterns of social relationships.

During the 1940s and 1950s, social network research developed through matrix algebra and graph theory, allowing the groups to be objectively identified within networks (Luce and Perry 1949). This led to work exploring concepts such as leadership, group cohesiveness, group productivity, cooperation, competition, communication and problem solving, and the spread of influence within groups (Borgatti et al. 2009; Freeman 2004). Around 1990, there was a massive rise of interest in networks, as other disciplines outside of sociology saw their potential, disciplines as diverse as physics and epidemiology (Wasserman and Faust 1994). A major contribution to network analysis was the characterization and modeling of small-world networks (Travers and Milgram 1969; Watts and Strogatz 1998). Small-world networks have been found in many settings including brain networks (Zhang et al. 2016) and food webs (Montoya and Solé 2002). Small-world networks display properties that transcend the characteristics of the individuals within it.

3 Social Network Concepts

SNA emphasizes patterns of relationships and interactions between network members (actors) rather than individual attributes. Actors can be individuals or entities such as departments or whole organizations, while relationships, which must be tightly defined, can be things such as collaboration, friendship, information exchange, or attendance at a particular event. While attribute data (e.g., gender, age, job position, seniority) is usually also collected, the focus is on this relational data that defines the network structure (Scott 2000). Different types of relational tie can lead to very different network structures; for example, a network of friendship ties between actors may be different from the same actors’ network of reporting ties.

Ties can be directional (e.g., providing information to, seeking advice from) or nondirectional (e.g., works in the same building, attend the same meeting). Ties can be recorded as simply present or absent or weighted to signify the weakness or strength of a relationship. This can be based on emotional intensity, level of reciprocity, or more usually frequency of contact (Granovetter 1973).

Relational tie data can be collected in different ways depending on the nature of the interaction. Face-to-face communication patterns may be directly observed (e.g., Obstfeld 2005). Referral patterns, email communications, or collaboration may be gathered using a self-report survey (Bishop and Waring 2012; Chan et al. 2016; Long et al. 2016) or documentary evidence (Fattore et al. 2009; Zheng et al. 2010).

SNA conceptualizes networks as composed of nodes (the actors in the group) and ties (the relationship between the actors) to generate sociograms. The ties form the structure of the network, and the nodes occupy positions within that structure. This proves a basis to investigate a wide range of issues, including communication pathways between actors (including gaps, bottlenecks, or opportunities to increase connectivity), the presence of “tribes” or silos, identification of key players, defining networks of social support, and revealing patterns of social influences on behavior. This also allows researchers to investigate relationships between network structures (e.g., communication flows) and important outcomes (e.g., rapid dissemination of ideas). Table 1 summarises key terms in social network analysis.
Table 1

Some social network terms and their definitions




A member of a network


An actor in a network that acts as an intermediary between two unlinked actors and clusters of actors


A strategy described by Burt (2005) of maximizing opportunities by increasing variation in the network through weak, bridging links to multiple, nonredundant contacts outside the group. This strategy contrasts with closure

Central actor

The actor who is nominated most often or who interacts with the most other members of a network


A measure of which actor or actors are the most connected or who interact with the most other actors


A strategy described by Burt (2005) of increasing cohesion by reducing variation within a group by forming strong links to members of the network. This strategy contrasts with brokerage


A subgroup of a network in which the local density of ties is higher than across the whole network


The process of spreading disease (in epidemiology), ideas, knowledge, or uptake of new technology through direct contact or social influence in social networks


The number of ties that actors have to other actors


The ratio of the number of ties present in a network divided by the number of possible ties

Directed tie

A tie that contains information about who initiated the tie and who receives it (e.g., information given by Actor A and received by Actor B)


Element of interest in a network. In a social network, it may be an individual or organization. In nonsocial networks, it may be an object, e.g., a station in a railway network

Edge (or tie)

A link or relationship between actors in a network shown on sociograms as a line


Focal actor in a network


Social network of a single focal actor


Defined by Rogers (2003) as the extent to which linked actors share similar attributes such as education, gender, or social status


A tie is said to be reciprocated when both actors acknowledge the tie

Social capital

A measure of the advantage that comes through social ties. May refer to the advantage held by an individual through their egonet (Burt 1992) or may refer to the quality of an entire group, e.g., an entire community (Putnam 1995)

Strength of tie

A measure of emotional intensity, level of reciprocity, or frequency of interaction associated with a tie

Strength of weak ties

A phenomenon described by Granovetter (1973) to describe the often advantageous, novel information that comes from weak links from outside of one’s closely tied network (who all tend to know the same information)

Tie (or edge)

A link or relationship between actors in a network shown on sociograms as a line

Undirected tie

A tie that does not require information about who initiated the tie or who received it (e.g., two actors on the same board, kinship ties)

Whole network survey

A survey that aims to elicit data from every member of the network, rather than a sample of members

Social network theory has been used to understand processes and phenomena across a range of different industries and settings including market competition (Burt 1992; Uzzi 1997), generation of innovative ideas (Bercovitz and Feldman 2011; Hargadon and Sutton 1997), influence and leadership (Lambright et al. 2010; Long et al. 2013b; Valente and Pumpuang 2007), and group dynamics (Balkundi et al. 2009; Susskind et al. 2011).

Within healthcare, social network theory and analysis have been used to look at coordination and integration of health services (e.g., Ayyalasomayajula et al. 2011; Khosla et al. 2016; Lower et al. 2010; Ryan et al. 2013), interprofessional communication and practice (e.g., Benham-Hutchins and Effken 2010; Chan et al. 2016; Creswick et al. 2009), strategies for translational research (e.g., Long et al. 2016; Rycroft-Malone et al. 2011), influence and leadership (e.g., Grimshaw et al. 2006; Kravitz et al. 2003), and quality and safety (e.g., Cunningham et al. 2012; Meltzer et al. 2010).

4 Structure Versus Agency

A debate within SNA research is the difference between two conceptualizations, usually referred to as structure and agency to explain human behavior and social networks. A structuralist view focuses on the recurring patterns of social interactions that appear to provide opportunities to an individual or constrain their behavior (Ansell et al. 2009). Agency, on the other hand, refers to an individual’s power to act and purposefully change their world (Apelrouth and Edles 2008).

A structuralist perspective of networks takes the view that a certain individual’s position in a network influences their actions (and consequences) as network positions afford certain opportunities. An actor in a central position in a network might be expected to have the same opportunities and constraints as another central actor in a different network. This approach focuses on the presence or absence of ties and tends to ignore the actual content of the ties (“ties conceptualised as girders” (Borgatti and Foster 2003, p.1003)). An example of this approach is a study of hospital facility managers (Heng et al. 2005) in which they illustrated through a sociogram that managers were situated centrally in the overall network between departments. This meant that they were able to act as coordinators and brokers between the many departments with which they linked.

An agency perspective perceives the actor taking a greater role and using the resources of the network to his or her own end. Agency-focused studies of networks try to understand how the individual’s actions and behavior have shaped their environment. This approach focuses on the nature of the ties, more specifically, on the resources that are delivered in the ties (“ties conceptualised as pipes” Borgatti and Foster 2003, p.1003). A small study by Kalish (2008) considered the personality traits of students in brokerage positions in a multicultural class to understand the nature of personal agency in defining their network position.

Networks are not static structures, so some studies have used both agency and structural perspectives in the same study. For example, Johnson et al. (2003) described the relationships between crew members at an Antarctic science base over three successive winters. As well as network structural data (“who hung out with who”), they observed the social roles that people took within the networks (“clown,” “leader who got things done”). By combining the data, they were able to describe the emergence and evolution of the network. Both viewpoints have merit and are inherently interesting to explore. Borgatti and Foster (2003), in their review of network research, however note that the vast majority of SNA studies take a structuralist perspective.

5 Methods

Social network data can be collected through self-report surveys, observation, or use of documentary data (e.g., emails, minutes of meetings). Before starting to collect data, the most important step is to define the relationship of interest. Referral or specific advice relationships may be straightforward, but for self-report surveys especially, the tie needs to be understood in the same way by all participants. Long et al. (2016), for example, used the following explanation of collaborative ties since collaboration is a multifaceted concept that had the potential to be understood in a number of different ways: “By ‘collaboration’ we mean either formally (e.g., on a funded project) or informally (e.g., have discussed aspects of research, supplied expertise, advice or equipment to others) … Please select those people with whom you are currently collaborating on a network activity, event or project …” (p. 6). This allowed the researchers to capture informal collaborative ties as well as the formal.

Two main methods of eliciting relationship data in the self-report survey method are roster style and name generator. If the boundaries of the network are known (e.g., people signed up to an online community of practice, staff on a ward, members of a committee), a roster of names may be used (pending ethical and governance approval). In the roster style survey, the members of the network are listed, and the respondent is asked to consider each person as a potential tie. In the name generator style of survey, the respondent is asked to write down the names of the people with whom they consider they have the defined tie without any prompting. This is useful if the membership of the network is not known (e.g., social support networks). The following resources provide detailed discussion of SNA methods and the various advantages and limitations associated with them (Borgatti et al. 2013; Scott 2000; Wasserman and Faust 1994).

6 Key Players in Collaborative Networks

Highly influential actors can be identified within networks, defined by their position in the overall structure. These actors are often called key players (Borgatti 2006), but there are a range of terms used in literature to describe them. Highly connected actors that occupy central positions in the network are termed opinion leaders (Gifford et al. 1999; Valente 2006; Valente and Pumpuang 2007), hubs (Buchanan 2003; Watts and Strogatz 1998), or connectors (Gladwell 2000 p.38). Moreno used the term communication “stars,” referring to actors who are chosen as friends by the most people (Moreno and Jennings 1934 p.72), and Allen used this to refer to actors who are approached most often for advice in a work setting (Allen 1970). Central actors appear to sit at the center of a star when ties are graphed (see Fig. 1a).
Fig. 1

(a) Star-shaped graph: the central actor is colored red. (b) Broker (in red) bridges two separate clusters of actors

Brokers are actors that link together individuals or groups of individuals (see Fig. 1b). They have been identified using a range of terms, the most common being bridges (Burt 1992; Valente and Fujimoto 2010), brokers (Cross and Prusak 2002; Gould and Fernandez 1989; Shi et al. 2009), and boundary spanners (Howse 2005; Tushman 1977). The broker is considered a key player as their position is inherently powerful; they may be the sole link between two noncommunicating groups. This can be used for a competitive advantage in business (e.g., having information from group A that group B does not, means the broker has a competitive edge) or to cause mischief (e.g., hoarding relevant information and not passing it along; acting as a gatekeeper and not allowing access to resources held by the other group). More positively, in collaborative networks, they can broker beneficial introductions, mediate between parties that are at odds, or provide a service of some kind to both parties (e.g., an interpreter, an expert).

Both key player roles have costs associated with them as well as advantages (Long et al. 2013c). Maintaining ties is a time-consuming exercise and beyond a certain number is unfeasible (Burt 1992, 2002).

7 Social Network Analysis and Healthcare Research

Social network analysis is a powerful approach to apply to healthcare settings. It can provide a framework to examine information flows, social and professional influence, and the phenomenon of siloed thinking and action (Long et al. 2016). While SNA has been well noted for its potential to map epidemiological phenomenon (e.g., the spread of HIV (Lin et al. 2012) or SARs (Chen et al. 2011)), over the past 10 years, it has also been increasingly taken up in research on healthcare organizations and systems. A number of reasons for this interest can be suggested. The increasing focus on the shape of social networks can be seen to follow from a concern with network forms of governance and policy attempts to engage with, and harness, embedded professional networks. Rather than an integrated hierarchy, it has increasingly been recognized that multiple “decentered” professional and organizational networks are involved in shaping and controlling health systems; SNA offers an approach to study such network forms.

A related concern of healthcare researchers is the nature of relationships between heterogeneous professional and occupational groups, how work is divided, and the implications for the coordination of care and fostering of collaboration. Rather than focusing on the aggregate relations, as has been common in perspectives such as sociology of the professions, SNA allow empirical investigation of patterns of relationships at the individual and subgroup level.

Third, an increasing concern of healthcare researchers over the past 15 years has been how knowledge, particularly new knowledge from research evidence and innovation, is translated and diffused into practice. SNA has also been used to examine the strategy of using translational research networks to bridge the “valley of death” (Butler 2008) between basic science and bedside, “real-life” practice. Again, SNA has shed light the patterns of relationships that underpin this process and how knowledge translation and improvement efforts can be supported. Two examples of author projects demonstrate recent applications.

Example 1 SNA of translational research strategies

Translational research undertakes the crucial role of moving biomedical discoveries out of the highly controlled laboratory environment and applying it in the complexity of patient and service delivery realities (Goldblatt and Lee 2010; Woolf 2008). Expertise and understanding through collaboration between both fields are necessary to achieve this, yet the gaps between research and clinical domains are widening through increased specialization and complexity (Schwartz and Vilquin 2003; Zerhouni 2005). Translational research networks are a strategy to facilitate collaboration by establishing a clear, joint vision and setting up an administrative structure to provide funding for joint projects, project officers, and shared resources as well as a social structure to maximize opportunities for collaboration, innovation, and knowledge exchange. While potential partners in such networks may abound, clusters within disciplines, professions, or geographic sites and the gaps between them may hinder their initiation. This study used SNA at baseline and three further points in time to examine changes in collaborative ties between members with reference to these clusters (Long et al. 2012, 2013a, b, 2014, 2016).

The translational research network of interest was established in late 2011, and initial membership was 68 cancer clinicians and researchers drawn from 6 hospital and university sites in New South Wales. An online, whole network survey was administered to all registered members of the network in early 2012, in 2013, and again in 2015. Membership changed in that time from 68 to 263 to 244 (respectively) as people joined or left. SNA showed that at baseline, ties of the original members were reflective of long-standing teaching and research arrangements and clustered by field (clinician or researcher) and by geographic proximity. Over the next 4 years, collaborative ties were shown to be bridging the field gap and including consumers in both research- and clinically based projects, although geographic proximity remained a feature. Key player analysis showed that the network manager was enacting a significant brokerage role in bringing new collaborative partners together, a quantitative finding that was confirmed through interviews (Long et al. 2013b).

In a similar project (unpublished Long and McDermott 2017), SNA was used to examine the growth of collaborative ties within a translational research network in the field of dementia. The network was shown, by the second year of operation, to have successfully brokered collaborations across formerly siloed sectors of academia, industry (largely staff in residential care facilities), consumers (people living with dementia and representatives from consumer advocacy groups), and government (policy-makers, regulators, and accreditation purveyors). Sociograms from the first survey at baseline (Fig. 2) and after 2 years of operation (Fig. 3) show this growth of intersectoral collaboration. External/internal (E/I) index analyses at the two points in time showed that at baseline, members from each sector were more likely to collaborate with people within their sector than with people in another sector, while after 2 years, members were more likely to collaborate with members outside their sector. In the last survey, there were 857 new ties (n = 121) described as “I have only worked with this person since joining the network.” Again, key player analysis showed both the centrality of the network manager and director and their brokerage roles.
Fig. 2

Baseline collaboration in a dementia translational research network (n = 104). The four sectors are shown by color: green = consumers, blue = academics, white = industry, pink = government. Gray nodes indicate missing sector data

Fig. 3

Collaboration ties after 2 years of operation of a dementia translational research network (n = 121). While academics outnumber other sectors, cross-sectoral collaboration was demonstrated quantitatively and is now more evident visually (Legend as for Fig. 2)

Example 2 Mixed methods SNA: relations between health and social care

The second example focuses on a study of knowledge sharing on issues of patient safety within a UK NHS hospital day surgery department. In light of well-recognized professional silos within health organizations (Waring 2004; Currie et al. 2008), this study aimed to investigate the patterns of knowledge sharing within and between professional groups. The methodology involved both a quantitative SNA survey and a period of ethnographic observations. The quantitative SNA survey was designed to elicit respondents’ close advice-giving contacts, asking respondents to provide named individuals within the department from whom they most commonly sought knowledge around patient safety, as well as the frequency of advice. Demographic data was also collected on the professional background, tenure, and work role of the respondent. Full network data was sought from all members of the department, identified both through an initial staff list and through following up new individuals identified in the name generator of respondents (n = 47, 85% response rate). Alongside this, 250 hours of ethnographic observations were undertaken, focusing on working practices and communication across settings within the department, as well as 40 qualitative interviews (see Bishop and Waring 2012).

Results from the study brought to light a number of key issues surrounding knowledge sharing within the department (see Fig. 4). Quantitative SNA results illustrated the complex web of intra- and interprofessional knowledge-sharing relationships in the department and highlighted (1) medics' position toward the periphery of the network, (2) the central role of senior nurses in the advice network of the department, (3) the peripheral role of part time and temporary staff, and (4) that a higher number of advice-seeking ties were held within professional groups than between groups. These findings supported prior theorizing in relation to trust and knowledge sharing within professional groups (Chan et al. 2016; Creswick et al. 2009). They also appeared to reflect aspects of service organization, for example, the central administrative role played by senior nurses within the department and medics attached to external departments of their clinical specialisms.
Fig. 4

Network of advice-seeking ties within a UK NHS hospital day surgery department

Alongside the quantitative SNA findings, the qualitative component of the study allowed further exploration of the patterns of advice giving within the quantitative SNA and provided insight into the meaning of the identified relationships. This work included examination of how work practice shaped the opportunities for interaction and hence knowledge sharing within and between groups. It also explored important factors shaping how individuals sought to negotiate relationships within the department while responding to conflicting demands. Bringing together quantitative SNA and qualitative research methods could, therefore, help to develop both an understanding of the structure of social relationship and the way these relationships are formed and maintained within the everyday practice of health organizations.

8 Conclusion and Future Directions

Researchers of health systems are increasingly recognizing that the socio-professional relationships are an essential component of quality, safety, and efficient delivery of care. SNA is a valuable tool to quantify these relationships at both an individual and organizational level. Patterns of collaboration, referral, and knowledge exchange are revealed by SNA and in combination with complementary qualitative methods such as ethnographic observation or interviews, fleshed out to give insight into social processes in healthcare. In addition, SNA is an important methodology for understanding emergent networks which have been shown to drive safety initiatives (Braithwaite et al. 2009).

SNA is an important methodology to analyze new social structures to drive policy and reform, cross-sectoral collaboration, integration of services, and dissemination of best practice. The use of SNA to reveal the utility of translational research networks as a strategy to create a common vision and broker-bridging relationships has been shown. SNA is also an important methodology for examining managed network structures as mechanisms of policy and reform. As public policy emphasizes dispersed leadership and accountability within networks, an understanding of the strength of relationships and how network roles such as brokerage are enacted is important. Further theory around network development and durability of relationships is another avenue for future research.


  1. Allen TJ. Communication networks in R & D labs. R & D Manag. 1970;1:14–21.CrossRefGoogle Scholar
  2. Ansell C, Reckhow S, Kelly A. How to reform a reform coalition: outreach, agenda expansion, and brokerage in Urban School Reform. Policy Stud J. 2009;37(4):717–43. Scholar
  3. Apelrouth S, Edles L. Classical and contemporary sociological theory: text and readings. Thousand Oaks: Pine Forge Press; 2008.Google Scholar
  4. Ayyalasomayajula B, Wiebe N, Hemmelgarn BR, Bello A, Manns B, Klarenbach S, Tonelli M. A novel technique to optimize facility locations of new nephrology services for remote areas. Clin J Am Soc Nephrol. 2011;6(9):2157–64. Scholar
  5. Balkundi P, Barsness Z, Michael JH. Unlocking the influence of leadership network structures on team conflict and viability. Small Group Res. 2009;40(3):301–22.CrossRefGoogle Scholar
  6. Benham-Hutchins MM, Effken JA. Multiprofessional patterns and methods of communication during patient handoffs. Int J Med Inform. 2010;79(4):252–67.CrossRefGoogle Scholar
  7. Bercovitz J, Feldman M. The mechanisms of collaboration in inventive teams: composition, social networks, and geography. Res Policy. 2011;40(1):81–93. Scholar
  8. Bishop S, Waring J. Discovering healthcare professional-practice networks: the added value of qualitative SNA. Qual Res Organ Manag Int J. 2012;7(3):308–22. Scholar
  9. Borgatti SP, Everett MG, Johnson JC. Analyzing social networks. Thousand Oaks: SAGE; 2013.Google Scholar
  10. Borgatti SP, Foster PC. The network paradigm in organizational research: a review and typology. J Manag. 2003;29(6):991–1013. Scholar
  11. Borgatti SP, Mehra A, Brass DJ, Labianca G. Network analysis in the social sciences. Science. 2009;323(5916):892–5.CrossRefGoogle Scholar
  12. Borgatti SP. Identifying sets of key players in a social network. Comput Math Organ Theory. 2006;12:21.CrossRefGoogle Scholar
  13. Braithwaite J, Runciman WB, Merry AF. Towards safer, better healthcare: harnessing the natural properties of complex sociotechnical systems. Qual Saf Health Care. 2009;18(1):37–41.CrossRefGoogle Scholar
  14. Buchanan M. Nexus: small worlds and the groundbreaking science of networks. New York: WW Norton; 2003.Google Scholar
  15. Burt RS. Bridge decay. Soc Networks. 2002;24(4):333–63.CrossRefGoogle Scholar
  16. Burt RS. Brokerage and closure: an introduction to social capital. New York: Oxford University Press; 2005.Google Scholar
  17. Burt RS. Structural holes: the social structure of competition. Cambridge, MA: Harvard University Press; 1992.Google Scholar
  18. Butler D. Crossing the valley of death. Nature. 2008;453:840–2.CrossRefGoogle Scholar
  19. Carmi S, Havlin S, Kirkpatrick S, Shavitt Y, Shir E. A model of Internet topology using k-shell decomposition. Proceedings of the National Academy of Sciences. 2007;104(27):11150–4.Google Scholar
  20. Chan B, Reeve E, Matthews S, Carroll P, Long JC, Held F, ⋯ Hilmer SN. Medicine information exchange networks among health care professionals and prescribing in geriatric medicine wards. Br J Clin Pharmacol. 2016;83(6):1185–96.
  21. Chen Y-D, Chen H, King C-C. Social network analysis for contact tracing. In: Castillo-Chavez C, Chen H, Lober WB, Thurmond M, Zeng D, editors. Infectious disease informatics and biosurveillance: research, systems and case studies. Boston: Springer US; 2011. p. 339–58.CrossRefGoogle Scholar
  22. Creswick N, Westbrook JI, Braithwaite J. Understanding communication networks in the emergency department. BMC Health Serv Res. 2009;9:247.CrossRefGoogle Scholar
  23. Cross R, Prusak L. The people who make organizations go – or stop. Harv Bus Rev. 2002;80:105–12.Google Scholar
  24. Cunningham FC, Ranmuthugala G, Plumb J, Georgiou A, Westbrook JI, Braithwaite J. Health professional networks as a vector for improving healthcare quality and safety: a systematic review. BMJ Qual Saf. 2012;21(3):239–49. Scholar
  25. Currie G, Waring J, Finn R. The limits of knowledge management for UK Public Services modernization: the case of patient safety and service quality. Public Administration. 2008;86(2):363–85.Google Scholar
  26. Durkheim E. Les Règles de la Méthode Sociologique. Paris: Revue philosophique; 1895.Google Scholar
  27. Fattore G, Frosini F, Salvatore D, Tozzi V. Social network analysis in primary care: the impact of interactions on prescribing behaviour. Health Policy. 2009;92(2–3):141–8.CrossRefGoogle Scholar
  28. Freeman LC. Network representations. In: Freeman LC, White DR, Romney AK, editors. Research methods in social network analysis. Fairfax, Virginia: George Mason University; 1989.Google Scholar
  29. Freeman LC. The development of social network analysis: a study in the sociology of science. Vancouver: Empirical Press; 2004.Google Scholar
  30. Fuller J, Kelly B, Sartore G, Fragar L, Tonna A, Pollard G, Hazell T. Use of social network analysis to describe service links for farmers’ mental health. Aust J Rural Health. 2007;15(2):99–106. Scholar
  31. Gifford D, Holloway R, Frankel M, Albright C, Meyerson R, Griggs R, et al. Improving adherence to dementia guidelines through education and opinion leaders. Ann Intern Med. 1999;131:237–46.CrossRefGoogle Scholar
  32. Gladwell M. The tipping point: how little thing can make a big difference. New York: Back Bay Books/Little, Brown and Company; 2000.Google Scholar
  33. Goldblatt EM, Lee W-H. From bench to bedside: the growing use of translational research in cancer medicine. Am J Transl Res. 2010;2(1):1–18.Google Scholar
  34. Gould RV, Fernandez RM. Structures of mediation: a formal approach to brokerage in transaction networks. Sociol Methodol. 1989;19:89–126.CrossRefGoogle Scholar
  35. Granovetter M. The strength of weak ties. Am J Sociol. 1973;78:1360–80.CrossRefGoogle Scholar
  36. Grimshaw J, Eccles M, Greener J, Maclennan G, Ibbotson T, Kahan J, Sullivan F. Is the involvement of opinion leaders in the implementation of research findings a feasible strategy? Implement Sci. 2006;1:3.CrossRefGoogle Scholar
  37. Haines M, Brown B, Craig J, D’Este C, Elliott E, Klineberg E, . . . Research Group, C. N. Determinants of successful clinical networks: the conceptual framework and study protocol. Implement Sci. 2012;7(1):16.Google Scholar
  38. Hargadon A, Sutton RI. Technology brokering and innovation in a product development firm. Adm Sci Q. 1997;42(4):716–49.CrossRefGoogle Scholar
  39. Heng HKS, McGeorge WD, Loosemore M. Beyond strategy: exploring the brokerage role of facilities manager in hospitals. J Health Organ Manag. 2005;19(1):16–31.CrossRefGoogle Scholar
  40. Howse EL. Factors that motivate hospital nurse middle managers to share knowledge related to boundary spanning roles. Ph.D., University of Toronto (Canada). 2005. Retrieved from
  41. Johnson JC, Boster JS, Palinkas LA. Social roles and the evolution of networks in extreme and isolated environments. J Math Sociol. 2003;27(2–3):89–121. Scholar
  42. Kalish Y. Bridging in social networks: who are the people in structural holes and why are they there? Asian J Soc Psychol. 2008;11(1):53–66.CrossRefGoogle Scholar
  43. Khosla N, Marsteller JA, Hsu YJ, Elliott DL. Analysing collaboration among HIV agencies through combining network theory and relational coordination. Soc Sci Med. 2016;150:85–94. Scholar
  44. Kravitz RL, Krackhardt D, Melnikow J, Franz CE, Gilbert WM, Zach A, ⋯ Romano PS. Networked for change? Identifying obstetric opinion leaders and assessing their opinions on caesarean delivery. Soc Sci Med. 2003;57(12):2423–34.Google Scholar
  45. Lambright KT, Mischen PA, Laramee CB. Building trust in public and nonprofit networks: personal, dyadic, and third-party influences. Am Rev Public Adm. 2010;40(1):64–82. Scholar
  46. Lin H, He N, Ding Y, Qiu D, Zhu W, Liu X, ⋯ Detels R. Tracing sexual contacts of HIV-infected individuals in a rural prefecture, Eastern China. BMC Public Health. 2012;12(1):533.
  47. Long JC, Cunningham FC, Braithwaite J. Network structure and the role of key players in a translational cancer research network: a study protocol. BMJ Open. 2012;2(3):e001434. Scholar
  48. Long JC, Cunningham FC, Carswell P, Braithwaite J. Who are the key players in a new translational research network? BMC Health Serv Res. 2013a;13:338. Scholar
  49. Long JC, Cunningham FC, Wiley J, Carswell P, Braithwaite J. Leadership in complex networks: the importance of network position and strategic action in a translational cancer research network. Implement Sci. 2013b;8:122. Scholar
  50. Long LC, Cunningham FC, Braithwaite J. Bridges, brokers and boundary spanners in collaborative networks: a systematic review. BMC Health Serv Res. 2013c;13(1).Google Scholar
  51. Long JC, Cunningham FC, Carswell P, Braithwaite J. Patterns of collaboration in complex networks: the example of a translational research network. BMC Health Serv Res. 2014;14(1):225. Scholar
  52. Long JC, Hibbert P, Braithwaite J. Structuring successful collaboration: a longitudinal social network analysis of a translational research network. Implement Sci. 2016;11:19. Scholar
  53. Long JC, McDermott S. Social Network analysis of a Dementia translational research network. 2017; unpublished dataGoogle Scholar
  54. Lower T, Fragar L, Depcynzksi J, Fuller J, Challinor K, Williams W. Social network analysis for farmers’ hearing services in a rural community. Aust J Prim Health. 2010;13(1):47–51.CrossRefGoogle Scholar
  55. Luce RD, Perry A. A method of matrix analysis of group structure. Psychometrika. 1949;14(2):95–116.CrossRefGoogle Scholar
  56. Meltzer D, Chung J, Khalili P, Marlow E, Arora V, Schumock G, Burt R. Exploring the use of social network methods in designing healthcare quality improvement teams. Soc Sci Med. 2010;71(6):1119–30.CrossRefGoogle Scholar
  57. Montoya JM, Solé RV. Small world patterns in food webs. J Theor Biol. 2002;214(3):405–12. Scholar
  58. Moreno JL, Jennings HH. Who shall survive? A new approach to the problem of human interrelations. Washington, DC: Nervous and Mental Disease Publishing Co; 1934.CrossRefGoogle Scholar
  59. Morita S, Oshio KI, Osana Y, Funabashi Y, Oka K, Kawamura K. Geometrical structure of the neuronal network of Caenorhabditis elegans. Physica A: Statistical Mechanics and its Applications 2001;298(3–4):553–561.Google Scholar
  60. Nasiruzzaman A. Complex network framework based comparative study of power grid centrality measures. Int J Electr Comput Eng. 2013;3(4):543.Google Scholar
  61. Obstfeld D. Social networks, the tertius iungens orientation, and involvement in innovation. Adm Sci Q. 2005;50:100–30.CrossRefGoogle Scholar
  62. Putnam R. Bowling alone: America’s declining social capital. J Democr. 1995;6(1):65–78.CrossRefGoogle Scholar
  63. Rogers E. Diffusion of innovations. 4th ed. New York: Free Press; 2003.Google Scholar
  64. Ryan DP, Puri M, Liu BA. Comparing patient and provider perceptions of home- and community-based services: social network analysis as a service integration metric. Home Health Care Serv Q. 2013;32(2):92–105. Scholar
  65. Rycroft-Malone J, Wilkinson J, Burton C, Andrews G, Ariss S, Baker R, ⋯ Thompson C. Implementing health research through academic and clinical partnerships: a realistic evaluation of the Collaborations for Leadership in Applied Health Research and Care (CLAHRC). Implement Sci. 2011;6(1):74.Google Scholar
  66. Schwartz K, Vilquin J-T. Building the translational highway: toward new partnerships between academia and the private sector. Nat Med. 2003;9(5):493–5.CrossRefGoogle Scholar
  67. Scott J. Social network analysis: a handbook. 2nd ed. London: Sage; 2000.Google Scholar
  68. Shi W, Markoczy L, Dess GG. The role of middle management in the strategy process: group affiliation, structural holes, and tertius iungens. J Manag. 2009;35(6):1453–80. Scholar
  69. Simmel G. The sociology of Georg Simmel (trans: Wolff KH). New York: Free Press; 1950.Google Scholar
  70. Susskind A, Odom-Reed P, Viccari A. Team leaders and team members in interorganizational networks: an examination of structural holes and performance. Commun Res. 2011;38(5):613–33. Scholar
  71. Travers J, Milgram S. An experimental study of a small world problem. Sociometry. 1969;32(4):425–43.CrossRefGoogle Scholar
  72. Tushman ML. Special boundary roles in the innovation process. Adm Sci Q. 1977;22(4):587–605.CrossRefGoogle Scholar
  73. Uzzi B. Social structure and competition in interfirm networks: the paradox of embeddedness. Adm Sci Q. 1997;42(1):35–67.CrossRefGoogle Scholar
  74. Valente T, Fujimoto K. Bridging: locating critical connectors in a network. Soc Networks. 2010;23:212–20.CrossRefGoogle Scholar
  75. Valente T, Pumpuang P. Identifying opinion leaders to promote behavior change. Health Educ Behav. 2007;34(6):881–96. Scholar
  76. Valente T. Opinion leader interventions in social networks. Br Med J. 2006;333(7578):1082–3. Scholar
  77. Waring JJ. A qualitative study of the intra-hospital variations in incident reporting. International J Quality in Health Care. 2004;16(5):347–352.Google Scholar
  78. Wasserman S, Faust K. Social network analysis. Cambridge: Cambridge University Press; 1994.CrossRefGoogle Scholar
  79. Wasserman S, Scott J, Carrington PJ. Introduction. In: Carrington PJ, Scott J, Wasserman S, editors. Models and methods in social network analysis. Cambridge, England: Cambridge University Press; 2005Google Scholar
  80. Watts DJ, Strogatz SH. Collective dynamics of ‘small-world’ networks. Nature. 1998;393(6684):440–2.CrossRefGoogle Scholar
  81. Wenger E, McDermott R, Snyder WM. Cultivating communities of practice. Boston: Harvard Business School Press; 2002.Google Scholar
  82. Woolf SH. The meaning of translational research and why it matters. JAMA. 2008;299(2):211–3. Scholar
  83. Zerhouni EA. Translational and clinical science: time for a new vision. N Engl J Med. 2005;353(15):1621–3. Scholar
  84. Zhang J, Lin X, Fu G, Sai L, Chen H, Yang J, ⋯ Yuan Z. Mapping the small-world properties of brain networks in deception with functional near-infrared spectroscopy. 2016;6:25297.
  85. Zheng K, Padman R, Krackhardt D, Johnson MP, Diamond HS. Social networks and physician adoption of electronic health records: insights from an empirical study. J Am Med Inform Assoc. 2010;17(3):328–36. Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Australian Institute of Health InnovationMacquarie UniversitySydneyAustralia
  2. 2.Centre for Health Innovation, Leadership and LearningNottingham University Business SchoolNottinghamUK

Personalised recommendations