Social network

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This article is about the theoretical concept as used in the social and behavioral sciences. For social networking sites, see Social networking service. For the 2010 movie, see The Social Network. For other uses, see Social network (disambiguation).
Evolution graph of a social network: Barabási model.
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A social network is a social structure made up of a set of social actors (such as individuals or organizations), sets of dyadic ties, and other social interactions between actors. The social network perspective provides a set of methods for analyzing the structure of whole social entities as well as a variety of theories explaining the patterns observed in these structures.[1] The study of these structures uses social network analysis to identify local and global patterns, locate influential entities, and examine network dynamics.

Social networks and the analysis of them is an inherently

Jacob Moreno is credited with developing the first sociograms in the 1930s to study interpersonal relationships. These approaches were mathematically formalized in the 1950s and theories and methods of social networks became pervasive in the social and behavioral sciences by the 1980s.[1][3] Social network analysis is now one of the major paradigms in contemporary sociology, and is also employed in a number of other social and formal sciences. Together with other complex networks, it forms part of the nascent field of network science.[4][5]

Overview

The social network is a

organizational studies, social psychology, sociology, and sociolinguistics
.

History

In the late 1890s, both

Gesellschaft, German, commonly translated as "society").[7] Durkheim gave a non-individualistic explanation of social facts, arguing that social phenomena arise when interacting individuals constitute a reality that can no longer be accounted for in terms of the properties of individual actors.[8] Georg Simmel, writing at the turn of the twentieth century, pointed to the nature of networks and the effect of network size on interaction and examined the likelihood of interaction in loosely knit networks rather than groups.[9]

Moreno's sociogram of a 2nd grade class

Major developments in the field can be seen in the 1930s by several groups in psychology, anthropology, and mathematics working independently.

S. F. Nadel codified a theory of social structure that was influential in later network analysis.[19] In sociology, the early (1930s) work of Talcott Parsons set the stage for taking a relational approach to understanding social structure.[20][21] Later, drawing upon Parsons' theory, the work of sociologist Peter Blau provides a strong impetus for analyzing the relational ties of social units with his work on social exchange theory.[22][23][24]

By the 1970s, a growing number of scholars worked to combine the different tracks and traditions. One group consisted of sociologist Harrison White and his students at the Harvard University Department of Social Relations. Also independently active in the Harvard Social Relations department at the time were Charles Tilly, who focused on networks in political and community sociology and social movements, and Stanley Milgram, who developed the "six degrees of separation" thesis.[25] Mark Granovetter[26] and Barry Wellman[27] are among the former students of White who elaborated and championed the analysis of social networks.[26][28][29][30]

Beginning in the late 1990s, social network analysis experienced work by sociologists, political scientists, and physicists such as

Nicholas A. Christakis, James H. Fowler
, and others, developing and applying new models and methods to emerging data available about online social networks, as well as "digital traces" regarding face-to-face networks.

Levels of analysis

Self-organization of a network, based on Nagler, Levina, & Timme (2011)[31]
Centrality

In general, social networks are

mutually exclusive, there are three general levels into which networks may fall: micro-level, meso-level, and macro-level
.

Micro level

At the micro-level, social network research typically begins with an individual, snowballing as social relationships are traced, or may begin with a small group of individuals in a particular social context.

Dyadic level: A dyad is a social relationship between two individuals. Network research on dyads may concentrate on structure of the relationship (e.g. multiplexity, strength), social equality, and tendencies toward reciprocity/mutuality.

Triadic level: Add one individual to a dyad, and you have a

balance and transitivity, as well as social equality and tendencies toward reciprocity/mutuality.[35] In the balance theory of Fritz Heider the triad is the key to social dynamics. The discord in a rivalrous love triangle is an example of an unbalanced triad, likely to change to a balanced triad by a change in one of the relations. The dynamics of social friendships in society has been modeled by balancing triads. The study is carried forward with the theory of signed graphs
.

Actor level: The smallest unit of analysis in a social network is an individual in their social setting, i.e., an "actor" or "ego." Egonetwork analysis focuses on network characteristics, such as size, relationship strength, density,

ethnographic kinship analysis or other genealogical
studies of relationships between individuals.

Subset level:

behavior.[38]

Meso level

In general, meso-level theories begin with a

population size that falls between the micro- and macro-levels. However, meso-level may also refer to analyses that are specifically designed to reveal connections between micro- and macro-levels. Meso-level networks are low density and may exhibit causal processes distinct from interpersonal micro-level networks.[39]

Social network diagram, meso-level

Organizations: Formal

organizations are social groups that distribute tasks for a collective goal.[40] Network research on organizations may focus on either intra-organizational or inter-organizational ties in terms of formal or informal relationships. Intra-organizational networks themselves often contain multiple levels of analysis, especially in larger organizations with multiple branches, franchises or semi-autonomous departments. In these cases, research is often conducted at a work group level and organization level, focusing on the interplay between the two structures.[40] Experiments with networked groups online have documented ways to optimize group-level coordination through diverse interventions, including the addition of autonomous agents to the groups.[41]

Randomly distributed networks:

attribute-based activity and popularity effects, as derived from explicit hypotheses about dependencies among network ties. Parameters are given in terms of the prevalence of small subgraph configurations in the network and can be interpreted as describing the combinations of local social processes from which a given network emerges. These probability models for networks on a given set of actors allow generalization beyond the restrictive dyadic independence assumption of micro-networks, allowing models to be built from theoretical structural foundations of social behavior.[42]

Examples of a random network and a scale-free network. Each graph has 32 nodes and 32 links. Note the "hubs" (large-degree nodes) in the scale-free diagram (on the right).

Scale-free networks: A

degree that greatly exceeds the average. The highest-degree nodes are often called "hubs", and may serve specific purposes in their networks, although this depends greatly on the social context. Another general characteristic of scale-free networks is the clustering coefficient distribution, which decreases as the node degree increases. This distribution also follows a power law.[44] The Barabási
model of network evolution shown above is an example of a scale-free network.

Macro level

Rather than tracing interpersonal interactions, macro-level analyses generally trace the outcomes of interactions, such as

population
.

Diagram: section of a large-scale social network

Large-scale networks:

computer sciences (see large-scale network mapping
).

Complex networks: Most larger social networks display features of

reciprocity, triad significance profile (TSP, see network motif), and other features. In contrast, many of the mathematical models of networks that have been studied in the past, such as lattices and random graphs, do not show these features.[45]

Theoretical links

Imported theories

Various theoretical frameworks have been imported for the use of social network analysis. The most prominent of these are Graph theory, Balance theory, Social comparison theory, and more recently, the Social identity approach.[46]

Indigenous theories

Few complete theories have been produced from social network analysis. Two that have are structural role theory and heterophily theory.

The basis of Heterophily Theory was the finding in one study that more numerous weak ties can be important in seeking information and innovation, as cliques have a tendency to have more homogeneous opinions as well as share many common traits. This homophilic tendency was the reason for the members of the cliques to be attracted together in the first place. However, being similar, each member of the clique would also know more or less what the other members knew. To find new information or insights, members of the clique will have to look beyond the clique to its other friends and acquaintances. This is what Granovetter called "the strength of weak ties".[47]

Structural holes

In the context of networks, social capital exists where people have an advantage because of their location in a network. Contacts in a network provide information, opportunities and perspectives that can be beneficial to the central player in the network. Most social structures tend to be characterized by dense clusters of strong connections.[48] Information within these clusters tends to be rather homogeneous and redundant. Non-redundant information is most often obtained through contacts in different clusters.[49] When two separate clusters possess non-redundant information, there is said to be a structural hole between them.[49] Thus, a network that bridges structural holes will provide network benefits that are in some degree additive, rather than overlapping. An ideal network structure has a vine and cluster structure, providing access to many different clusters and structural holes.[49]

Networks rich in structural holes are a form of social capital in that they offer information benefits. The main player in a network that bridges structural holes is able to access information from diverse sources and clusters.[49] For example, in business networks, this is beneficial to an individual's career because he is more likely to hear of job openings and opportunities if his network spans a wide range of contacts in different industries/sectors. This concept is similar to Mark Granovetter's theory of weak ties, which rests on the basis that having a broad range of contacts is most effective for job attainment. Structural holes have been widely applied in social network analysis, resulting in applications in a wide range of practical scenarios as well as machine learning-based social prediction.[50]

Research clusters

Art Networks

Research has used network analysis to examine networks created when artists are exhibited together in museum exhibition. Such networks have been shown to affect an artist's recognition in history and historical narratives, even when controlling for individual accomplishments of the artist.[51][52] Other work examines how network grouping of artists can affect an individual artist's auction performance.[53] An artist's status has been shown to increase when associated with higher status networks, though this association has diminishing returns over an artist's career.

Communication

literary studies, and semiotics. Many communication concepts describe the transfer of information from one source to another, and can thus be conceived of in terms of a network. Social network analysis has thus been successfully applied to phenomena ranging from the social diffusion of linguistic innovation[54] to the influence of peer learner communication on study abroad second language acquisition.[55]

Community

In J.A. Barnes' day, a "

social network services. Such devices and services require extensive and ongoing maintenance and analysis, often using network science methods. Community development
studies, today, also make extensive use of such methods.

Complex networks

Complex networks require methods specific to modelling and interpreting social complexity and complex adaptive systems, including techniques of dynamic network analysis
. Mechanisms such as Dual-phase evolution explain how temporal changes in connectivity contribute to the formation of structure in social networks.

Conflict and Cooperation

The study of social networks is being used to examine the nature of interdependencies between actors and the ways in which these are related to outcomes of conflict and cooperation. Areas of study include cooperative behavior among participants in

protests
; promotion of peaceful behavior,
communities
through networks of informal governance; the role of social networks in both intrastate conflict and interstate conflict; and social networking among politicians, constituents, and bureaucrats.[56]

Criminal networks

In criminology and urban sociology, much attention has been paid to the social networks among criminal actors. For example, murders can be seen as a series of exchanges between gangs. Murders can be seen to diffuse outwards from a single source, because weaker gangs cannot afford to kill members of stronger gangs in retaliation, but must commit other violent acts to maintain their reputation for strength.[57]

Diffusion of innovations

Diffusion of ideas and innovations studies focus on the spread and use of ideas from one actor to another or one culture and another. This line of research seeks to explain why some become "early adopters" of ideas and innovations, and links social network structure with facilitating or impeding the spread of an innovation. A case in point is the social diffusion of linguistic innovation such as neologisms.[54]

Demography

In demography, the study of social networks has led to new sampling methods for estimating and reaching populations that are hard to enumerate (for example, homeless people or intravenous drug users.) For example, respondent driven sampling is a network-based sampling technique that relies on respondents to a survey recommending further respondents.[58][59]

Economic sociology

The field of sociology focuses almost entirely on networks of outcomes of social interactions. More narrowly, economic sociology considers behavioral interactions of individuals and groups through social capital and social "markets". Sociologists, such as Mark Granovetter, have developed core principles about the interactions of social structure, information, ability to punish or reward, and trust that frequently recur in their analyses of political, economic and other institutions. Granovetter examines how social structures and social networks can affect economic outcomes like hiring, price, productivity and innovation and describes sociologists' contributions to analyzing the impact of social structure and networks on the economy.[60]

Health care

Analysis of social networks is increasingly incorporated into

systems.[61]

Human ecology

transdisciplinary study of the relationship between humans and their natural, social, and built environments. The scientific philosophy of human ecology has a diffuse history with connections to geography, sociology, psychology, anthropology, zoology, and natural ecology.[62][63]

Language and linguistics

Studies of language and linguistics, particularly evolutionary linguistics, focus on the development of linguistic forms and transfer of changes, sounds or words, from one language system to another through networks of social interaction. Social networks are also important in language shift, as groups of people add and/or abandon languages to their repertoire. This may happen through the social diffusion of linguistic innovation,[54] and through second language acquisition via communication with peers.[55]

Literary networks

In the study of literary systems, network analysis has been applied by Anheier, Gerhards and Romo,

Even-Zohar, can be integrated with network theory and the relationships between different actors in the literary network, e.g. writers, critics, publishers, literary histories, etc., can be mapped using visualization
from SNA.

Organizational studies

Research studies of formal or informal organization relationships, organizational communication, economics, economic sociology, and other resource transfers. Social networks have also been used to examine how organizations interact with each other, characterizing the many informal connections that link executives together, as well as associations and connections between individual employees at different organizations.[68] Many organizational social network studies focus on teams.[69] Within team network studies, research assesses, for example, the predictors and outcomes of centrality and power, density and centralization of team instrumental and expressive ties, and the role of between-team networks. Intra-organizational networks have been found to affect organizational commitment,[70] organizational identification,[37] interpersonal citizenship behaviour.[71]

Social capital

trust, and cooperation, and market agents produce goods and services not mainly for themselves, but for a common good. Social capital is split into three dimensions: the structural, the relational and the cognitive dimension. The structural dimension describes how partners interact with each other and which specific partners meet in a social network. Also, the structural dimension of social capital indicates the level of ties among organizations.[72] This dimension is highly connected to the relational dimension which refers to trustworthiness, norms, expectations and identifications of the bonds between partners. The relational dimension explains the nature of these ties which is mainly illustrated by the level of trust accorded to the network of organizations.[72] The cognitive dimension analyses the extent to which organizations share common goals and objectives as a result of their ties and interactions.[72]

Social capital is a sociological concept about the value of social relations and the role of cooperation and confidence to achieve positive outcomes. The term refers to the value one can get from their social ties. For example, newly arrived immigrants can make use of their social ties to established migrants to acquire jobs they may otherwise have trouble getting (e.g., because of unfamiliarity with the local language). A positive relationship exists between social capital and the intensity of social network use.[73][74][75] In a dynamic framework, higher activity in a network feeds into higher social capital which itself encourages more activity.[73][76]

Advertising

This particular cluster focuses on brand-image and promotional strategy effectiveness, taking into account the impact of customer participation on sales and brand-image. This is gauged through techniques such as sentiment analysis which rely on mathematical areas of study such as data mining and analytics. This area of research produces vast numbers of commercial applications as the main goal of any study is to understand consumer behaviour and drive sales.

Network position and benefits

In many

organizations, members tend to focus their activities inside their own groups, which stifles creativity and restricts opportunities. A player whose network bridges structural holes has an advantage in detecting and developing rewarding opportunities.[48] Such a player can mobilize social capital by acting as a "broker" of information between two clusters that otherwise would not have been in contact, thus providing access to new ideas, opinions and opportunities. British philosopher and political economist John Stuart Mill, writes, "it is hardly possible to overrate the value of placing human beings in contact with persons dissimilar to themselves.... Such communication [is] one of the primary sources of progress."[77]
Thus, a player with a network rich in structural holes can add value to an organization through new ideas and opportunities. This in turn, helps an individual's career development and advancement.

A social capital broker also reaps control benefits of being the facilitator of information flow between contacts. Full communication with exploratory mindsets and information exchange generated by dynamically alternating positions in a social network promotes creative and deep thinking.[78] In the case of consulting firm Eden McCallum, the founders were able to advance their careers by bridging their connections with former big three consulting firm consultants and mid-size industry firms.[79] By bridging structural holes and mobilizing social capital, players can advance their careers by executing new opportunities between contacts.

There has been research that both substantiates and refutes the benefits of information brokerage. A study of high tech Chinese firms by Zhixing Xiao found that the control benefits of structural holes are "dissonant to the dominant firm-wide spirit of cooperation and the information benefits cannot materialize due to the communal sharing values" of such organizations.[80] However, this study only analyzed Chinese firms, which tend to have strong communal sharing values. Information and control benefits of structural holes are still valuable in firms that are not quite as inclusive and cooperative on the firm-wide level. In 2004, Ronald Burt studied 673 managers who ran the supply chain for one of America's largest electronics companies. He found that managers who often discussed issues with other groups were better paid, received more positive job evaluations and were more likely to be promoted.[48] Thus, bridging structural holes can be beneficial to an organization, and in turn, to an individual's career.

Social media

Main article: Social media

electronic commerce, information exchanged may also correspond to exchanges of money, goods or services in the "real" world.[81] Social network analysis
methods have become essential to examining these types of computer mediated communication.

In addition, the sheer size and the volatile nature of social media has given rise to new network metrics. A key concern with networks extracted from social media is the lack of robustness of network metrics given missing data.[82]

See also

References

  1. ^
    ISBN 9780521387071
    .
  2. ISBN 978-0-13-195893-7
    .
  3. ISBN 978-1-59457-714-7
    .
  4. S2CID 522293
    .
  5. ISBN 978-0-521-19533-1
    .
  6. ^ a b c Scott, John P. (2000). Social Network Analysis: A Handbook (2nd edition). Thousand Oaks, CA: Sage Publications.
  7. ^ Tönnies, Ferdinand (1887). Gemeinschaft und Gesellschaft, Leipzig: Fues's Verlag. (Translated, 1957 by Charles Price Loomis as Community and Society, East Lansing: Michigan State University Press.)
  8. ^ Durkheim, Emile (1893). De la division du travail social: étude sur l'organisation des sociétés supérieures, Paris: F. Alcan. (Translated, 1964, by Lewis A. Coser as The Division of Labor in Society, New York: Free Press.)
  9. ^ Simmel, Georg (1908). Soziologie, Leipzig: Duncker & Humblot.
  10. ISBN 978-1-84787-395-8
    .
  11. ISBN 978-0-7619-6339-4
    .
  12. ^ Malinowski, Bronislaw (1913). The Family Among the Australian Aborigines: A Sociological Study. London: University of London Press.
  13. ^ Radcliffe-Brown, Alfred Reginald (1930) The social organization of Australian tribes. Sydney, Australia: University of Sydney Oceania monographs, No.1.
  14. JSTOR 2844197
    .
  15. ^ Lévi-Strauss, Claude ([1947]1967). Les structures élémentaires de la parenté. Paris: La Haye, Mouton et Co. (Translated, 1969 by J. H. Bell, J. R. von Sturmer, and R. Needham, 1969, as The Elementary Structures of Kinship, Boston: Beacon Press.)
  16. ^ Barnes, John (1954). "Class and Committees in a Norwegian Island Parish". Human Relations, (7): 39–58.
  17. ^ Freeman, Linton C.; Wellman, Barry (1995). "A note on the ancestral Toronto home of social network analysis". Connections. 18 (2): 15–19.
  18. S2CID 145286556
    .
  19. ^ Nadel, S. F. 1957. The Theory of Social Structure. London: Cohen and West.
  20. ^ Parsons, Talcott ([1937] 1949). The Structure of Social Action: A Study in Social Theory with Special Reference to a Group of European Writers. New York: The Free Press.
  21. ^ Parsons, Talcott (1951). The Social System. New York: The Free Press.
  22. ^ Blau, Peter (1956). Bureaucracy in Modern Society. New York: Random House, Inc.
  23. ^ Blau, Peter (1960). "A Theory of Social Integration". The American Journal of Sociology, (65)6: 545–556, (May).
  24. ^ Blau, Peter (1964). Exchange and Power in Social Life.
  25. ^ Bernie Hogan. "The Networked Individual: A Profile of Barry Wellman". Archived from the original on 2012-07-12. Retrieved 2012-06-15.
  26. ^ a b Granovetter, Mark (2007). "Introduction for the French Reader". Sociologica. 2: 1–8.
  27. ^ Wellman, Barry (1988). "Structural analysis: From method and metaphor to theory and substance". pp. 19–61 in B. Wellman and S. D. Berkowitz (eds.) Social Structures: A Network Approach, Cambridge, UK: Cambridge University Press.
  28. ^ Mullins, Nicholas. Theories and Theory Groups in Contemporary American Sociology. New York: Harper and Row, 1973.
  29. ^ Tilly, Charles, ed. An Urban World. Boston: Little Brown, 1974.
  30. ^ Wellman, Barry. 1988. "Structural Analysis: From Method and Metaphor to Theory and Substance". pp. 19–61 in Social Structures: A Network Approach, edited by Barry Wellman and S. D. Berkowitz. Cambridge: Cambridge University Press.
  31. S2CID 2809783
    .
  32. ^ Newman, Mark, Albert-László Barabási and Duncan J. Watts (2006). The Structure and Dynamics of Networks (Princeton Studies in Complexity). Oxford: Princeton University Press.
  33. S2CID 140433919
    .
  34. ISBN 978-0521382694.{{cite book}}: CS1 maint: multiple names: authors list (link
    )
  35. ^ a b Kadushin, C. (2012). Understanding social networks: Theories, concepts, and findings. Oxford: Oxford University Press.
  36. S2CID 40359730
    .
  37. ^
    doi:10.1002/job.694
    .
  38. ISBN 978-1-4614-1800-9
    .
  39. S2CID 3609428. Archived
    (PDF) from the original on 2016-03-04. Retrieved 2012-02-26.
  40. ^
    S2CID 144857162
    .
  41. PMID 28516927
    .
  42. doi:10.1093/pan/mpq037
    .
  43. S2CID 45651735
    .
  44. ^ Barabási, Albert-László (2003). Linked: how everything is connected to everything else and what it means for business, science, and everyday life. New York: Plum.
  45. PMID 11258382
    .
  46. ^ Kilduff, M.; Tsai, W. (2003). Social networks and organisations. Sage Publications.
  47. S2CID 59578641
    .
  48. ^
    S2CID 2152743
    .
  49. ^ a b c d Burt, Ronald (1992). Structural Holes: The Social Structure of Competition. Cambridge, MA: Harvard University Press.
  50. doi:10.1109/TCSS.2021.3070321
    .
  51. ISSN 0304-422X
    .
  52. ISSN 0002-7642
    .
  53. hdl:1765/119341
    .
  54. ^
    doi:10.2478/v10057-012-0010-z
    .
  55. ^
    doi:10.1017/S0261444820000580
    .
  56. doi:10.1146/annurev-polisci-041719-102523
    .
  57. S2CID 24605697. Archived from the original
    (PDF) on 7 April 2014. Retrieved 29 March 2013.
  58. S2CID 67695078. Archived
    from the original on 31 January 2022. Retrieved 21 September 2021.
  59. doi:10.1146/annurev-soc-060116-053556
    .
  60. S2CID 263519132
    .
  61. ^ Levy, Judith and Bernice Pescosolido (2002). Social Networks and Health. Boston, MA: JAI Press.
  62. ^ Crona, Beatrice and Klaus Hubacek (eds.) (2010). "Special Issue: Social network analysis in natural resource governance" Archived 2012-06-04 at the Wayback Machine. Ecology and Society, 48.
  63. ^ Ernstson, Henrich (2010). "Reading list: Using social network analysis (SNA) in social-ecological studies". Resilience Science Archived 2012-04-03 at the Wayback Machine
  64. S2CID 143587142
    .
  65. doi:10.1016/S0304-422X(03)00035-4
    .
  66. ^ Senekal, B. A. (2012). "Die Afrikaanse literêre sisteem: ʼn Eksperimentele benadering met behulp van Sosiale-netwerk-analise (SNA)". LitNet Akademies. 9: 3.
  67. S2CID 241976819, archived
    from the original on 2023-02-04, retrieved 2022-03-16
  68. JSTOR 2657354
    .
  69. doi:10.1177/0149206320901573
    .
  70. S2CID 145641976
    .
  71. PMID 16435939
    .
  72. ^ a b c (Claridge, 2018).
  73. ^
    S2CID 222233101. Archived
    from the original on 2021-06-09. Retrieved 2021-06-09.
  74. doi:10.1111/j.1083-6101.2009.01474.x
    .
  75. S2CID 144525876
    .
  76. ISSN 0378-8733
    .
  77. ^ Mill, John (1909). Principles of Political Economy. Library of Economics and Liberty: William J Ashley.
  78. S2CID 14419926
    .
  79. ^ Gardner, Heidi; Eccles, Robert (2011). "Eden McCallum: A Network Based Consulting Firm". Harvard Business School Review.
  80. ^ Xiao, Zhixing; Tsui, Anne (2007). "When Brokers May Not Work: The Cultural Contingency of Social Archived 2016-09-14 at the Wayback Machine Capital in Chinese High-tech Firms". Administrative Science Quarterly.
  81. S2CID 29051307
    .
  82. S2CID 18520393
    .

Further reading

External links

Organizations

Peer-reviewed journals

Textbooks and educational resources

Data sets

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