Social+Network+Analysis

= Social Network Analysis =

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**Definition**
Social network analysis (SNA) is a method used to understand the social patterns of bounded social groups, such as individuals, communities, organizations, countries, webpages, and articles. Most research focuses on the individual and organizational levels of inquiry. Network analysis is the mapping and measuring of flows between people, groups, organizations, and categories. It is the search for structures of relations between actors, which can produce both qualitative and quantitative data using a variety of collection methods. Nodes are used to represent actors and connect to other nodes by ties (lines). The ties and positionality of nodes in relation to the entire network are used for analysis.

**Relevant Characteristics**
Network analysis allows researchers to study individual social actors, as well as the relationships among those actors. This method is beneficial to researches interested in the structure of relations among interconnected parts of a whole system. Network research can be bound by both groups (i.e. nation, community, tribe) and social categories (i.e. class, gender, ethnicity). Measurements are taken of linkages between these social units including material and non-material transfers. The resulting data is expressed in relational terms. Sampling is typically avoided in network analysis. Categories of relations between nodes are determined by the four following principles: **Similarities** are used to assess nodes with shared variables, such as demographics, attitudes, and organization membership. These attributes exist within the social structure not the individual. **Social relations** are used to classify people by their relationships to one another, such as friend, acquaintance, and kin. **Interactions** are used to depict behaviors between nodes, the behaviors occur between social relations. **Flows** depict information and resources that move between nodes. Researchers are interested in how these relations affect the **strength and weakness** of **ties between nodes** and the **positionality** of the nodes within the network. Mechanisms between nodes include **transmission**- a flow of some materials from node to node, **adaption**- nodes change in a similar manner due to the same pressures in the social environment, **binding**- social ties bind nodes together creating a new social entity, and **exclusion**- two nodes tie together in a competitive environment and exclude the third node.

• Actor (Node):  a discrete individual or collective social unit  (friendship, exchange, membership, communication) • Relational Tie:  that which establishes a link between actors
 * Key concepts include: **

• Dyad:  a relational tie between two actors • Triad:  a relation tie between three actors • Subgroup:  any subset of actors and the ties among them • Group:  a collection of actors and their ties being measured  • Social Network: finite set(s) of actors and the relations among them <span style="font-family: Arial,sans-serif; font-size: 10pt;">• Relation: <span style="font-family: Arial,sans-serif; font-size: 10pt;"> a collection of specific ties among a group

**“Method Made Easy”**
1. Identify research question 2<span style="font-family: Arial,sans-serif; font-size: 10pt;">. Gather background information on the network 3. Identify network boundaries (who will you include and who will you exclude?) <span style="font-family: Arial,sans-serif; font-size: 10pt;">4. Define objectives and clarify scope of analysis <span style="font-family: Arial,sans-serif; font-size: 10pt;">5. Develop hypotheses and related questions <span style="font-family: Arial,sans-serif; font-size: 10pt;">5. Choose data collection method(s) <span style="font-family: Arial,sans-serif; font-size: 10pt;">6. Collect data using various methods- use appropriate data collection tools to understand relation between nodes (interviews, surveys, Figure 3 archival and historical data, participant observation) <span style="font-family: Calibri,sans-serif; font-size: 11pt;">7) <span style="font-family: Arial,sans-serif; font-size: 10pt;">Use software applications, such as ANTHROPAC, Pajek, Cytoscape, GUESS, ORA, or UCINET to map network and analyze data



**Advantages**
There are numerous advantages to using SNA. The most important advantage is the rich contextual data that the method generates. Researchers are able to transform micro level data into a macro pattern, <span style="font-family: Arial,sans-serif; font-size: 10pt;">by explaining relational ties in a larger social structure and it is readily applied to different populations and/or social categories. This allows for a visual representation of the social world that cannot be depicted through transcriptions or frequencies alone. D <span style="font-family: Arial,sans-serif; font-size: 10pt;">ata is relatively easy to obtain for Network Research. Analysis can also provide qualitative and quantitative measures using a variety of data collection methods already familiar to social scientists.

**Limitations**
SNA has several limitations. The first is a clear lack of consistent methodology; the literature on the subject is very technical and requires wading through multiple sources with varying foci and language. There is also a general discordance in the literature as to whether SNA is a method or a perspective. SNA has been criticized for a lack of clear theoretical underpinnings. In addition, the technical expertise needed to use SNA software and analysis may also create limitations for researches.<span style="font-family: Arial,sans-serif; font-size: 10pt;"> Data loads can be very heavy, depending upon the network under study. In dealing with such extensive and complex information, many visual models can be extremely difficult to interpret. Furthermore, charts and maps generated by SNA may also be virtually unreadable to the public without clear explanations. <span style="font-family: Arial,sans-serif; font-size: 10pt;">Network Analysis can also be limited by not providing a fully adequate model for explaining the actual formation, reproduction, and transformation of social networks themselves. Thus, it encounters difficulty when trying to describe agency on an individual level. The use of SNA may be ethically questionable in research situations where illicit or dangerous activities<span style="font-family: Arial,sans-serif; font-size: 10pt;">.

**Analysis**
<span style="font-family: Arial,sans-serif; font-size: 10pt;">Visual representations are used to interpret and convey network data as closed populations (complete networks), or the relationships surrounding individual units (egocentric networks). Data is obtained from various methods and described qualitatively and/or quantitatively. Social networks are then visually modeled with software applications that are capable of complex predictive analysis and manipulation. Software programs include Ucinet, Gephi, and InFlow, as well as others.

**Method in Context**
SNA is deeply rooted in the disciplines of sociology and mathematics. The inception of social network thinking is evident in Durkheim’s proposal that social systems are similar to biological systems, both created of complex interrelated parts. The most recent predecessor to SNA is dated to 1932 when Moreno used ‘sociometry’ as a method to map the social relations of runaway girls from a school in upstate New York. In the 1940’s a paradigm shift occurred that influenced much of the modeling SNA uses today, graph theory and matrix algebra began to be used to formally chart social networks. <span style="font-family: Arial,sans-serif; font-size: 10pt;">Jacob Moreno was a Network Research pioneer. In 1953 he developed sociographs <span style="font-family: Arial,sans-serif; font-size: 10pt;"> to visually describe the relationships among small groups. Before sociometry <span style="font-family: Arial,sans-serif; font-size: 10pt;">, no one knew what the social structure of a population really looked like. During the 1970s, rapid theoretical development occurred along with the appearance of computers. Network Analysis has evolved far beyond small groups and been firmly established as an interdisciplinary specialty. Stanley Milgram’s (1967) “small-world experiment” is an example of this method in context; it later inspired the popular game “six degrees of Kevin Bacon”. Surely, the growing influence of the Internet and social networking is sure to offer promising opportunities for the application of this method.

**Online Resources**
You Tube: []
 * Ending up on the Wrong Side of the Tracks: Valdis Krebs at TEDxRiga

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 * The Formation of Economic and Social Networks

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 * Gephi: The Open Graph Viz Platform

<span style="color: windowtext; font-family: Arial,sans-serif; font-size: 10pt;">[|www.insna.org]
 * International Network for Social Network Analysis

<span style="color: windowtext; font-family: Arial,sans-serif; font-size: 10pt;">[|www.faculty.ucr.edu/~hanneman/nettext/]
 * Introduction to Network Analysis Methods

<span style="color: windowtext; font-family: Arial,sans-serif; font-size: 10pt;">www.kstoolkit.org/Social+Network+Analysis
 * Knowledge Sharing Toolkit–Social Network Analysis

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 * Online PowerPoint by Dr. Giorgos Cheliotis (National University of Singapore)

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 * SNA & ONA Projects, Case Studies

<span style="color: windowtext; font-family: Arial,sans-serif; font-size: 10pt;">[|www.hsph.harvard.edu/massconect/files/sna_resource_list.pdf]
 * Social Network Analysis Resource List

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 * UCINET Open Source Software

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 * University of Michigan SNA Online Course

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 * The Vancouver Network Analysis Team

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 * Victor M. Preciado–Online Courses and Lectures

**Further Reading**
<span style="font-family: Arial,sans-serif; font-size: 10pt;">Burt, Ronald S. and Michael J. Minor <span style="font-family: Arial,sans-serif; font-size: 10pt;">1983 Applied Network Analysis: A Methodological Introduction. Beverly Hills: Sage Publications.
 * //<span style="font-family: Arial,sans-serif; font-size: 10pt; line-height: 1.5;">A methodological reference book for research on the social environments of people, groups, or formal organizations of actors //

<span style="font-family: Arial,sans-serif; font-size: 10pt;">Carrington, P., with J. Scott, J, and S. Wasserman, ed. <span style="font-family: Arial,sans-serif; font-size: 10pt;">2005 Models and Methods in Social Network Analysis. New York: Cambridge University Press.
 * //<span style="font-family: Arial,sans-serif; font-size: 10pt; line-height: 1.5;">This edited volume presents significant contributions to the development of quantitative models and methods that are beneficial to the analysis of social networks. This volume also discusses concepts such as network measurement and network sampling. //

<span style="font-family: Arial,sans-serif; font-size: 10pt;">Carrington, Peter and John Scott, eds. <span style="font-family: Arial,sans-serif; font-size: 10pt;">2011 Social Network Analysis: An Introduction in Handbook of Social Network Analysis. London: Sage //<span style="font-family: Arial,sans-serif; font-size: 10pt;">in social network analysis, which discusses concepts, methods, topics, and debates. //
 * //<span style="font-family: Arial,sans-serif; font-size: 10pt; line-height: 1.5;">This handbook is a valuable resource for researchers, students, and teachers interested //

<span style="font-family: Arial,sans-serif;">Emirbayer, Mustafa and Jeff Goodwin

<span style="font-family: Arial,sans-serif;">1994 Network Analysis, Culture, and the Problem of Agency. American Journal of Sociology 99(6):1411–1454.

<span style="font-family: Arial,sans-serif; font-size: 10pt;">Freeman, Linton C., with Douglas. R. White, and A. Kimball Romney, ed. <span style="font-family: Arial,sans-serif; font-size: 10pt;">1989 Research Methods in Social Network Analysis. Fairfax: George Mason University Press.
 * <span style="font-family: Arial,sans-serif; font-size: 10pt;"> An edited volume assembled from a conference on methods of research in social networks sponsored by the University of California, Irvine, Research Program in Social Network Analysis

<span style="font-family: Arial,sans-serif;">Grant, Felix

<span style="font-family: Arial,sans-serif;">2010 Data Analysis: Social Networks. //In Scientific Computing World// <span style="color: windowtext; font-family: Arial,sans-serif; font-size: 10pt;">[]

<span style="font-family: Arial,sans-serif; font-size: 10pt;">Knoke, David and Song Yank <span style="font-family: Arial,sans-serif; font-size: 10pt;"> 2008 Social Network Analysis. Sage Publications
 * //<span style="font-family: Arial,sans-serif; font-size: 10pt; line-height: 1.5;">This text provides an updated discussion on the changes within the field that have furthered social network analysis as a method. This book addresses key concepts, topics, data collection techniques, and options for analysis. //

<span style="font-family: Arial,sans-serif;">Marsden, Peter V. V. <span style="font-family: Arial,sans-serif;">1990 Network Data and Measurement. Annual review of sociology 16(1):435-463.

<span style="font-family: Arial,sans-serif;">Staab, S. <span style="font-family: Arial,sans-serif;">2005 Social Networks Applied. IEEE intelligent systems & their applications 20(1):80-80.

<span style="font-family: Arial,sans-serif;">Walker, Barry E. <span style="font-family: Arial,sans-serif;">1993 Statistical models for social support networks. Sociological methods & research 22(1):71-98.

<span style="font-family: Arial,sans-serif; font-size: 10pt;">Wasserman, Stanley and Joseph Galaskiewicz, ed. <span style="font-family: Arial,sans-serif; font-size: 10pt;">1994 Advances in Social Network Analysis: Research in Social and Behavioral Sciences. Thousand Oaks: Sage Publications.
 * //<span style="font-family: Arial,sans-serif; font-size: 10pt;"> An edited volume showing how social network analysis has been used to advance substantive research in the social and behavioral sciences //

<span style="font-family: Arial,sans-serif;">Wellman, B. <span style="font-family: Arial,sans-serif;">1983 Network Analysis: Some Basic Principles. Sociological Theory 1(1):155-200.

<span style="font-family: Arial,sans-serif;">Wellman, Barry, and S. D. Berkowits, ed. <span style="font-family: Arial,sans-serif;">1988 Social Structures. New York: Cambridge University Press.