A social network is a dyadic ties between these 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. 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. 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.
- Overview 1
- History 2
Levels of analysis 3
- Micro level 3.1
- Meso level 3.2
- Macro level 3.3
Theoretical links 4
- Imported theories 4.1
- Indigenous theories 4.2
Structural holes 5
- Information benefits 5.1
- Social capital mobility benefits 5.2
Research clusters 6
- Communications 6.1
- Community 6.2
- Complex networks 6.3
- Criminal networks 6.4
- Diffusion of innovations 6.5
- Demography 6.6
- Economic sociology 6.7
- Health care 6.8
- Human ecology 6.9
- Language and linguistics 6.10
- Literary networks 6.11
- Organizational studies 6.12
- Social capital 6.13
- Social media 6.14
- See also 7
- References 8
- Further reading 9
External links 10
- Organizations 10.1
- Peer-reviewed journals 10.2
- Textbooks and educational resources 10.3
- Data sets 10.4
In the late 1890s, both
- Pajek's list of lists of datasets
- UC Irvine Network Data Repository
- Stanford Large Network Dataset Collection
- M.E.J. Newman datasets
- Pajek datasets
- Gephi datasets
- KONECT - Koblenz network collection
- RSiena datasets
- Networks, Crowds, and Markets (2010) by D. Easley & J. Kleinberg
- Introduction to Social Networks Methods (2005) by R. Hanneman & M. Riddle
- Social Network Analysis Instructional Web Site by S. Borgatti
Textbooks and educational resources
- Social Networks
- Network Science
- Journal of Social Structure
- Journal of Mathematical Sociology
- Social Network Analysis and Mining (SNAM)
- "Connections". Toronto: International Network for Social Network Analysis.
- International Network for Social Network Analysis
- Wellman, Barry; Berkowitz, S.D. (1988). Social Structures: A Network Approach. Structural Analysis in the Social Sciences. Cambridge University Press.
- Scott, John (1991). Social Network Analysis: a handbook. SAGE.
- Wasserman, Stanley; Faust, Katherine (1994). Social Network Analysis: Methods and Applications. Structural Analysis in the Social Sciences. Cambridge University Press.
- Barabási, Albert-László (2003). Linked: How everything is connected to everything else and what it means for business, science, and everyday life. Plum.
- Freeman, Linton C. (2004). The Development of Social Network Analysis: A Study in the Sociology of Science. Empirical Press.
- Barnett, George A. (2011). Encyclopedia of Social Networks. SAGE.
- Kadushin, Charles (2012). Understanding Social Networks: Theories, Concepts, and Findings. Oxford University Press.
- Rainie, Lee and Barry Wellman. (2012). Networked: The New Social Operating System. MIT Press. ISBN 978-0262017190
- Estrada, E. (2011). The Structure of Complex Networks: Theory and Applications. Oxford University Press. ISBN 978-0-199-59175-6
- Aneja, Nagender and Gambhir, Sapna. (2013). .Ad-hoc-Social-Network-A-Comprehensive-Survey
- Wasserman, Stanley; Faust, Katherine (1994). "Social Network Analysis in the Social and Behavioral Sciences". Social Network Analysis: Methods and Applications. Cambridge University Press. pp. 1–27.
- Scott, W. Richard; Davis, Gerald F. (2003). "Networks In and Around Organizations". Organizations and Organizing. Pearson Prentice Hall.
- Freeman, Linton (2004). The Development of Social Network Analysis: A Study in the Sociology of Science. Empirical Press.
- Borgatti, Stephen P.; Mehra, Ajay; Brass, Daniel J.; Labianca, Giuseppe (2009). "Network Analysis in the Social Sciences". Science 323 (5916): 892–895.
- Easley, David; Kleinberg, Jon (2010). "Overview". Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge University Press. pp. 1–20.
- Scott, John P. (2000). Social Network Analysis: A Handbook (2nd edition). Thousand Oaks, CA: Sage Publications.
- 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.)
- 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.)
- Simmel, Georg (1908). Soziologie, Leipzig: Duncker & Humblot.
- For a historical overview of the development of social network analysis, see: Carrington, Peter J. & Scott, John (2011). "Introduction". The Sage Handbook of Social Network Analysis. SAGE. p. 1.
- See also the diagram in Scott, John (2000). Social Network Analysis: A Handbook. SAGE. p. 8.
- Malinowski, Bronislaw (1913). The Family Among the Australian Aborigines: A Sociological Study. London: University of London Press.
- Radcliffe-Brown, Alfred Reginald (1930) The social organization of Australian tribes. Sydney, Australia: University of Sydney Oceania monographs, No.1.
- Radcliffe-Brown, A.R. (1940). "On social structure". Journal of the Royal Anthropological Institute, 70, 1-12.
- Lévi-Strauss, Claude (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.)
- Barnes, John (1954). "Class and Committees in a Norwegian Island Parish." Human Relations, (7): 39-58.
- Freeman, Linton C. and Barry Wellman (1995). "A note on the ancestoral Toronto home of social network analysis." Connections, 18(2): 15-19.
- Savage, Mike (2008). "Elizabeth Bott and the formation of modern British sociology." The Sociological Review, 56(4): 579–605.
- Nadel, SF. 1957. The Theory of Social Structure. London: Cohen and West.
- Parsons, Talcott ( 1949). The Structure of Social Action: A Study in Social Theory with Special Reference to a Group of European Writers. New York, NY: The Free Press.
- Parsons, Talcott (1951). The Social System. New York, NY: The Free Press.
- Blau, Peter (1956). Bureaucracy in Modern Society. New York: Random House, Inc.
- Blau, Peter (1960). "A Theory of Social Integration." The American Journal of Sociology, (65)6: 545-556 , (May).
- Blau, Peter (1964). Exchange and Power in Social Life.
- Bernie Hogan. "The Networked Individual: A Profile of Barry Wellman".
- Granovetter, Mark (2007). "Introduction for the French Reader," Sociologica 2: 1–8
- 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.
- Mullins, Nicholas. Theories and Theory Groups in Contemporary American Sociology. New York: Harper and Row, 1973.
- Tilly, Charles, ed. An Urban World. Boston: Little Brown, 1974.
- Mark Granovetter, "Introduction for the French Reader," Sociologica 2 (2007): 1–8.
- 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.
- Nagler, Jan, Anna Levina and Marc Timme (2011). "Impact of single links in competitive percolation". Nature Physics 7: 265–270.
- Newman, Mark, Albert-László Barabási and Duncan J. Watts (2006). The Structure and Dynamics of Networks (Princeton Studies in Complexity). Oxford: Princeton University Press.
- Wellman, Barry (2008). "Review: The development of social network analysis: A study in the sociology of science." Contemporary Sociology, 37: 221-222.
- Faust, Stanley Wasserman; Katherine (1998). Social network analysis : methods and applications (Reprint. ed.). Cambridge [u.a.]: Cambridge Univ. Press.
- Kadushin, C. (2012). Understanding social networks: Theories, concepts, and findings. Oxford: Oxford University Press.
- Granovetter, M. (1976). Network sampling: Some first steps. American Journal of Sociology 81 (6). pp. 1287–1303.
- Jones, C. & Volpe, E.H. (2011). Organizational identification: Extending our understanding of social identities through social networks. Journal of Organizational Behavior, 32, 413-434.
- de Nooy, Wouter (2012). "Graph Theoretical Approaches to Social Network Analysis." in Computational Complexity: Theory, Techniques, and Applications (Robert A. Meyers, ed.). Springer. pp. 2864–2877.
- Hedström, Peter, Rickard Sandell, and Charlotta Stern (2000)."Mesolevel Networks and the Diffusion of Social Movements: The Case of the Swedish Social Democratic Party.", 106(1): 145–72.American Journal of Sociology
- Riketta, M. & Nienber, S. (2007). Multiple identities and work motivation: The role of perceived compatibility between nested organizational units. British Journal of Management, 18, S61-77.
- Cranmer, Skyler J. and Bruce A. Desmarais (2011). "Inferential Network Analysis with Exponential Random Graph Models." Political Analysis, 19(1): 66-86.
- Moreira, André A., Demétrius R. Paula, Raimundo N. Costa Filho, José S. Andrade, Jr. (2006). "Competitive cluster growth in complex networks". Physical Review E 73 (6).
- Barabási, Albert-László (2003). Linked: how everything is connected to everything else and what it means for business, science, and everyday life. New York, NY: Plum.
- Strogatz, Steven H. (2001). "Exploring complex networks". Nature 410 (6825): 268–276.
- Kilduff, M., Tsai, W. (2003). Social networks and organisations. Sage Publications.
- Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology 78 (6). pp. 1360–1380.
- Burt, Ronald (2004). "Structural Holes and Good Ideas". American Journal of Sociology.
- Burt, Ronald (1992). Structural Holes: The Social Structure of Competition. Cambridge, MA: Harvard University Press.
- Mill, John (1909). Principles of Political Economy. Library of Economics and Liberty: William J Ashley.
- Gardner, Heidi; Eccles, Robert (2011). "Eden McCallum: A Network Based Consulting Firm". Harvard Business School Review.
- Xiao, Zhixing; Tsui, Anne (2007). "When Brokers May Not Work: The Cultural Contingency of Social Capital in Chinese High-tech Firms". Administrative Science Quarterly.
- Papachristos, Andrew (2009). "Murder by Structure: Dominance Relations and the Social Structure of Gang Homicide". American Journal of Sociology (The University of Chicago Press) 115 (1): 74–128.
- Granovetter, Mark (2005). "The Impact of Social Structure on Economic Outcomes." The Journal of Economic Perspectives, 19(1): 33-50
- Levy, Judith and Bernice Pescosolido (2002). Social Networks and Health. Boston, MA: JAI Press.
- Crona, Beatrice and Klaus Hubacek (eds.) (2010). "Special Issue: Social network analysis in natural resource governance." , 48.Ecology and Society
- Ernstson, Henrich (2010). "Reading list: Using social network analysis (SNA) in social-ecological studies." Resilience Science
- Anheier, H.K., J. Gerhards en F.P. Romo. 1995. Forms of capital and social structure of fields: examining Bourdieu’s social topography. American Journal of Sociology, 100:859–903
- De Nooy, W. Fields and networks: Correspondence analysis and social network analysis in the framework of Field Theory. Poetics, 31:305–27
- Senekal, B. A. 2012. Die Afrikaanse literêre sisteem: ŉ Eksperimentele benadering met behulp van Sosiale-netwerk-analise (SNA), LitNet Akademies 9(3)
- Podolny, J.M. & Baron, J.N. (1997). Resources and relationships: Social networks and mobility in the workplace. American Sociological Review, 62(5), 673-693
- Lee, J. & Kim, S. (2011). Exploring the role of social networks in affective organizational commitment: Network centrality, strength of ties, and structural holes. The American Review of Public Administration, 41(2), 205-223.
- Bowler, W.M. & Brass, D.J. (2011). Relational correlates of interpersonal citizenship behaviour: A social network perspective. Journal of Applied Psychology, 91(1), 70-82.
- Sebastián, Valenzuela; Namsu Park; Kerk F. Kee (2009). "Is There Social Capital in a Social Network Site? Facebook Use and College Students' Life Satisfaction, Trust, and Participation". Journal of Computer-Mediated Communication 14 (4): 875–901.
- Wang, Hua and Barry Wellman (2010). "Social Connectivity in America: Changes in Adult Friendship Network Size from 2002 to 2007". American Behavioral Scientist 53 (8): 1148–69.
- Garton, Laura, Caroline Haythornthwaite, and Barry Wellman (23 June 2006). "Studying Online Social Networks". Journal of Computer-Mediated Communication 3 (1).
- Collective network
- Complex networks
- Dynamic network analysis
- International Network for Social Network Analysis
- Interpersonal relationship
- Network science
- Network society
- Network theory
- Semiotics of social networking
- Scientific collaboration network
- Social complexity
- Social group
- Social media
- Social network analysis
- Social Network (sociolinguistics)
- Social networking
- Social relation
- Social web
Computer networks combined with social networking software produces a new medium for social interaction. A relationship over a computerized social networking service can be characterized by context, direction, and strength. The content of a relation refers to the resource that is exchanged. In a computer mediated communication context, social pairs exchange different kinds of information, including sending a data file or a computer program as well as providing emotional support or arranging a meeting. With the rise of electronic commerce, information exchanged may also correspond to exchanges of money, goods or services in the “real” world. Social network analysis methods have become essential to examining these types of computer mediated communication.
Social capital is a sociological concept which refers to 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). Studies show that a positive relationship exists between social capital and the intensity of social network use.
Research studies of interpersonal citizenship behaviour.
In the study of literary systems, network analysis has been applied by Anheier, Gerhards and Romo, De Nooy, and Senekal, to study various aspects of how literature functions. The basic premise is that polysystem theory, which has been around since the writings of 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.
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.
Language and linguistics
Human ecology is an interdisciplinary and 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.
Analysis of social networks is increasingly incorporated into systems.
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.
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.
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.
Diffusion of innovations
In criminology and urban sociology, much attention has been paid to the social networks among criminal actors. For example, Andrew Papachristos  has studied gang murders 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.
In J.A. Barnes' day, a "community" referred to a specific geographic location and studies of community ties had to do with who talked, associated, traded, and attended church with whom. Today, however, there are extended "online" communities developed through telecommunications devices and 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.
Communication Studies are often considered a part of both the social sciences and the humanities, drawing heavily on fields such as sociology, psychology, anthropology, information science, biology, political science, and economics as well as rhetoric, literary studies, and semiotics. Many communications concepts describe the transfer of information from one source to another, and can thus be conceived of in terms of a network.
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. 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. Thus, bridging structural holes can be beneficial to an organization, and in turn, to an individual’s career.
A social capital broker also reaps control benefits of being the facilitator of information flow between contacts. In the case of consulting firm Eden McCallum, the founders were able to advance their careers by bridging their connections with former big 3 consulting firm consultants and mid-size industry firms. By bridging structural holes and mobilizing social capital, players can advance their careers by executing new opportunities between contacts.
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. 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.” 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.
Social capital mobility benefits
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. 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.
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. Information within these clusters tends to be rather homogeneous and redundant. Non-redundant information is most often obtained through contacts in different clusters. When two separate clusters possess non-redundant information, there is said to be a structural hole between them. 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.
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."
Few complete theories have been produced from social network analysis. Two that have are Structural Role Theory and Heterophily Theory.
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.
Complex networks: Most larger social networks display features of social complexity, which involves substantial non-trivial features of network topology, with patterns of complex connections between elements that are neither purely regular nor purely random (see, complexity science, dynamical system and chaos theory), as do biological, and technological networks. Such complex network features include a heavy tail in the degree distribution, a high clustering coefficient, assortativity or disassortativity among vertices, community structure, and hierarchical structure. In the case of agency-directed networks these features also include 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.
Large-scale networks: Large-scale network is a term somewhat synonymous with "macro-level" as used, primarily, in social and behavioral sciences, in economics. Originally, the term was used extensively in the computer sciences (see large-scale network mapping).
Scale-free networks: A scale-free network is a network whose degree distribution follows a power law, at least asymptotically. In network theory a scale-free ideal network is a random network with a degree distribution that unravels the size distribution of social groups. Specific characteristics of scale-free networks vary with the theories and analytical tools used to create them, however, in general, scale-free networks have some common characteristics. One notable characteristic in a scale-free network is the relative commonness of vertices with 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. The Barabási model of network evolution shown above is an example of a scale-free network.
Randomly distributed networks: Exponential random graph models of social networks became state-of-the-art methods of social network analysis in the 1980s. This framework has the capacity to represent social-structural effects commonly observed in many human social networks, including general degree-based structural effects commonly observed in many human social networks as well as reciprocity and transitivity, and at the node-level, homophily and 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.
: Formal Organizations
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.
Subset level: Subset levels of network research problems begin at the micro-level, but may cross over into the meso-level of analysis. Subset level research may focus on distance and reachability, cliques, cohesive subgroups, or other group actions or behavior. 
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, centrality, prestige and roles such as isolates, liaisons, and bridges. Such analyses, are most commonly used in the fields of psychology or social psychology, ethnographic kinship analysis or other genealogical studies of relationships between individuals.
Triadic level: Add one individual to a dyad, and you have a triad. Research at this level may concentrate on factors such as balance and transitivity, as well as social equality and tendencies toward reciprocity/mutuality.
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.
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.
In general, social networks are emergent, and complex, such that a globally coherent pattern appears from the local interaction of the elements that make up the system. These patterns become more apparent as network size increases. However, a global network analysis of, for example, all interpersonal relationships in the world is not feasible and is likely to contain so much information as to be uninformative. Practical limitations of computing power, ethics and participant recruitment and payment also limit the scope of a social network analysis. The nuances of a local system may be lost in a large network analysis, hence the quality of information may be more important than its scale for understanding network properties. Thus, social networks are analyzed at the scale relevant to the researcher's theoretical question. Although levels of analysis are not necessarily mutually exclusive, there are three general levels into which networks may fall: micro-level, meso-level, and macro-level.
Levels of analysis
Major developments in the field can be seen in the 1930s by several groups in psychology, anthropology, and mathematics working independently. In psychology, in the 1930s, Jacob L. Moreno began systematic recording and analysis of social interaction in small groups, especially classrooms and work groups (see sociometry). In anthropology, the foundation for social network theory is the theoretical and ethnographic work of Bronislaw Malinowski, Alfred Radcliffe-Brown, and Claude Lévi-Strauss. A group of social anthropologists associated with Max Gluckman and the Manchester School, including John A. Barnes, J. Clyde Mitchell and Elizabeth Bott Spillius, often are credited with performing some of the first fieldwork from which network analyses were performed, investigating community networks in southern Africa, India and the United Kingdom. Concomitantly, British anthropologist S.F. Nadel codified a theory of social structure that was influential in later network analysis. In sociology, the early (1930s) work of Talcott Parsons set the stage for taking a relational approach to understanding social structure. 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. 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.Mark Granovetter and Barry Wellman are among the former students of White who elaborated and championed the analysis of social networks.