User:Tangeny/social network

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Social network

Introduction

online communities
and instant messaging.

Social network analysis (SNA) views

social relationships in terms of network theory consisting of nodes and ties (also called edges, links, or connections). Nodes are the individual actors within the networks, and ties are the relationships between the actors. The resulting graph-based structures are often very complex
. There can be many kinds of ties between the nodes. Research in a number of academic fields has shown that social networks operate on many levels, from families up to the level of nations, and play a critical role in determining the way problems are solved, organizations are run, and the degree to which individuals succeed in achieving their goals.

In its simplest form, a social network is a map of specified ties, such as friendship, between the nodes being studied. The nodes to which an individual is thus connected are the social contacts of that individual. The network can also be used to measure

network diagram
, where nodes are the points and ties are the lines.

Social network analysis

An example of a social network diagram. The node with the highest betweenness centrality is marked in yellow.

Social network analysis (related to

organizational studies, social psychology, and sociolinguistics
, and has become a popular topic of speculation and study.

People have used the idea of "social network" loosely for over a century to connote complex sets of relationships between members of social systems at all scales, from interpersonal to international. In 1954, J. A. Barnes started using the term systematically to denote patterns of ties, encompassing concepts traditionally used by the public and those used by social scientists: bounded

Ronald Burt, Kathleen Carley, Martin Everett, Katherine Faust, Linton Freeman, Mark Granovetter, David Knoke, David Krackhardt, Peter Marsden, Nicholas Mullins, Anatol Rapoport, Stanley Wasserman, Barry Wellman, Douglas R. White, and Harrison White expanded the use of systematic social network analysis.[1]

Social network analysis has now moved from being a suggestive metaphor to an analytic approach to a paradigm, with its own theoretical statements, methods, social network analysis software, and researchers. Analysts reason from whole to part; from structure to relation to individual; from behavior to attitude. They typically either study whole networks (also known as complete networks), all of the ties containing specified relations in a defined population, or personal networks (also known as egocentric networks), the ties that specified people have, such as their "personal communities".[2] In the latter case, the ties are said to go from egos, who are the focal actors who are being analyzed, to their alters. The distinction between whole/complete networks and personal/egocentric networks has depended largely on how analysts were able to gather data. That is, for groups such as companies, schools, or membership societies, the analyst was expected to have complete information about who was in the network, all participants being both potential egos and alters. Personal/egocentric studies were typically conducted when identities of egos were known, but not their alters. These studies rely on the egos to provide information about the identities of alters and there is no expectation that the various egos or sets of alters will be tied to each other. A snowball network refers to the idea that the alters identified in an egocentric survey then become egos themselves and are able in turn to nominate additional alters. While there are severe logistic limits to conducting snowball network studies, a method for examining hybrid networks has recently been developed in which egos in complete networks can nominate alters otherwise not listed who are then available for all subsequent egos to see.[3] The hybrid network may be valuable for examining whole/complete networks that are expected to include important players beyond those who are formally identified. For example, employees of a company often work with non-company consultants who may be part of a network that cannot fully be defined prior to data collection.

Several analytic tendencies distinguish social network analysis:[4]

There is no assumption that groups are the building blocks of society: the approach is open to studying less-bounded social systems, from nonlocal
communities to links among websites
.
Rather than treating individuals (persons, organizations, states) as discrete units of analysis, it focuses on how the structure of ties affects individuals and their relationships.
In contrast to analyses that assume that socialization into norms determines behavior, network analysis looks to see the extent to which the structure and composition of ties affect norms.

The shape of a social network helps determine a network's usefulness to its individuals. Smaller, tighter networks can be less useful to their members than networks with lots of loose connections (

weak ties) to individuals outside the main network. More open networks, with many weak ties and social connections, are more likely to introduce new ideas and opportunities to their members than closed networks with many redundant ties. In other words, a group of friends who only do things with each other already share the same knowledge and opportunities. A group of individuals with connections to other social worlds is likely to have access to a wider range of information. It is better for individual success to have connections to a variety of networks rather than many connections within a single network. Similarly, individuals can exercise influence or act as brokers within their social networks by bridging two networks that are not directly linked (called filling structural holes).[5]

The power of social network analysis stems from its difference from traditional social scientific studies, which assume that it is the attributes of individual actors—whether they are friendly or unfriendly, smart or dumb, etc.—that matter. Social network analysis produces an alternate view, where the attributes of individuals are less important than their relationships and ties with other actors within the network. This approach has turned out to be useful for explaining many real-world phenomena, but leaves less room for individual agency, the ability for individuals to influence their success, because so much of it rests within the structure of their network.

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. For example, power within organizations often comes more from the degree to which an individual within a network is at the center of many relationships than actual job title. Social networks also play a key role in hiring, in business success, and in job performance. Networks provide ways for companies to gather information, deter competition, and collude in setting prices or policies.[6]

The positive and negative impacts of
social networking

Positive impacts

Stay Connected

Online

Social networking
sites bring people with common interests together, offer exposure to new ideas from around the world, and lower inhibitions to overcome social anxiety.

Sharing News

Social networking sites are updated in real time, people can communicate information to each quickly and effectively such as updated facts about upcoming or current events, recentannouncements and invitations.

Using Media

Sites can be used as a tool to distribute a wide range of media to the public audience. For example, in schools, photos and videos of recent events can be displayed on the website page to offer parents who could not attend the events the chance to see their students in action, school-wide celebrations and student achievements.

Meet New People

People can get to know each other through social sites by exchanging residential information and numbers, of where they can be found. As in a recent event that happened here in Namibia were Facebook reconciles the long lost father with her daughter whereby the young girl joined Facebook and try to search for her long lost father that she never seen ever since she was born, and as for now they are happy together as father and daughter.[7]

Conduct Business

Online social networks have a superior role in the business arena. Online

communities such as eBay or Craig’s List enables users to sell and buy products online. In so doing it creates great convenience to trade and conduct with people from all over the world. As globalization is increasing,businesses that take on one project after another, are likely to outsource their employees from project to project.[8]

Teaching Tools

Social media can be used to enhance the learning experience outside the classroom. 'Teachers' can use these technology tools to post learning videos, assignments or other information from the classroom. Videos of recent meetings and assemblies can be posted to give parents necessary information about the latest happenings and decisions within the school.

Negative impacts

Theft

Some

social networking
profiles, such as birthdays, pet names, mothers' maiden names, names of children, and other details often used in passwords and security questions.

Social Depression, Loneliness, and Depression

  • conflict
    conflict

Spending much time on the Internet leads to social isolation and loneliness, which in turn leads to depression.

e-mail
and instant messaging. This leads to people hating each other and saying bad things or insulting others online were everyone can see. Too little integration with society or a community leads to suicide, people got to feel that life is meaningless, or there is nothing better to do, so they end up killing themselves. They are constantly mourning on their own dead inner selves.

Instant Messaging

Instant messaging causes social isolation and disembodiment because people are simply having conversations that are not real. They start losing social skills in the real world as they master their social skills chatting behind a

Social networking
sites can cause personality and brain disorders in children, such as the inability to have real conversations, limited attention spans, or a need for instant gratification.

Time consuming

The hours per day of face-to-face socializing have declined as the use of social media has increased. People who use these sites frequently are prone to social isolation. Parents spend less time with their children and couples spend less time together even when they live in the same house, because they are using the Internet instead of interacting with each other.



History of social network analysis

A summary of the progress of social networks and social network analysis has been written by Linton Freeman.[9]

Precursors of social networks in the late 1800s include Émile Durkheim and Ferdinand Tönnies. Tönnies argued that social groups can exist as personal and direct social ties that either link individuals who share values and belief (gemeinschaft) or impersonal, formal, and instrumental social links (gesellschaft). 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. He distinguished between a traditional society – "mechanical solidarity" – which prevails if individual differences are minimized, and the modern society – "organic solidarity" – that develops out of cooperation between differentiated individuals with independent roles.

Georg Simmel, writing at the turn of the twentieth century, was the first scholar to think directly in social network terms. His essays pointed to the nature of network size on interaction and to the likelihood of interaction in ramified, loosely-knit networks rather than groups (Simmel, 1908/1971).

After a hiatus in the first decades of the twentieth century, three main traditions in social networks appeared. In the 1930s,

Radcliffe-Brown's presidential address to British anthropologists urged the systematic study of networks.[10]
However, it took about 15 years before this call was followed-up systematically.

Social network analysis developed with the kinship studies of Elizabeth Bott in

S.F. Nadel codified a theory of social structure that was influential in later network analysis.[11]

In the 1960s-1970s, a growing number of scholars worked to combine the different tracks and traditions. One group was centered around Harrison White and his students at the Harvard University Department of Social Relations: Ivan Chase, Bonnie Erickson, Harriet Friedmann, Mark Granovetter, Nancy Howell, Joel Levine, Nicholas Mullins, John Padgett, Michael Schwartz and Barry Wellman. 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.[12] Mark Granovetter and Barry Wellman are among the former students of White who have elaborated and popularized social network analysis.[13]

Significant independent work was also done by scholars elsewhere:

University of California Irvine social scientists interested in mathematical applications, centered around Linton Freeman, including John Boyd, Susan Freeman, Kathryn Faust, A. Kimball Romney and Douglas White; quantitative analysts at the University of Chicago, including Joseph Galaskiewicz, Wendy Griswold, Edward Laumann, Peter Marsden, Martina Morris, and John Padgett; and communication scholars at Michigan State University, including Nan Lin and Everett Rogers. A substantively-oriented University of Toronto sociology group developed in the 1970s, centered on former students of Harrison White: S.D. Berkowitz, Harriet Friedmann, Nancy Leslie Howard, Nancy Howell, Lorne Tepperman and Barry Wellman, and also including noted modeler and game theorist Anatol Rapoport.In terms of theory, it critiqued methodological individualism and group-based analyses, arguing that seeing the world as social networks offered more analytic leverage.[14]

Research

Social network analysis has been used in epidemiology to help understand how patterns of human contact aid or inhibit the spread of diseases such as HIV in a population. The evolution of social networks can sometimes be modeled by the use of agent based models, providing insight into the interplay between communication rules, rumor spreading and social structure.

SNA may also be an effective tool for mass surveillance – for example the Total Information Awareness program was doing in-depth research on strategies to analyze social networks to determine whether or not U.S. citizens were political threats.

opinion leaders
often play major roles in spurring the adoption of innovations, although factors inherent to the innovations also play a role.

free riders
", as it may be easier in larger groups to take advantage of the benefits of living in a community without contributing to those benefits.

Mark Granovetter found in one study that more numerous weak ties can be important in seeking information and innovation. 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".

Guanxi (关系）is a central concept in Chinese society (and other East Asian cultures) that can be summarized as the use of personal influence. The word is usually translated as "relation," "connection" or "tie" and is used in as broad a variety of contexts as are its English counterparts. However, in the context of interpersonal relations, Guanxi (关系）is loosely analogous to "clout" or "pull" in the West. Guanxi can be studied from a social network approach.[15]

The

small world phenomenon is the hypothesis that the chain of social acquaintances required to connect one arbitrary person to another arbitrary person anywhere in the world is generally short. The concept gave rise to the famous phrase six degrees of separation after a 1967 small world experiment by psychologist Stanley Milgram. In Milgram's experiment, a sample of US individuals were asked to reach a particular target person by passing a message along a chain of acquaintances. The average length of successful chains turned out to be about five intermediaries or six separation steps (the majority of chains in that study actually failed to complete). The methods (and ethics as well) of Milgram's experiment were later questioned by an American scholar, and some further research to replicate Milgram's findings found that the degrees of connection needed could be higher.[16] Academic researchers continue to explore this phenomenon as Internet-based communication technology has supplemented the phone and postal systems available during the times of Milgram. A recent electronic small world experiment at Columbia University found that about five to seven degrees of separation are sufficient for connecting any two people through e-mail.[17]

social network graphs
can be predicted.

One study has found that happiness tends to be correlated in social networks. When a person is happy, nearby friends have a 25 percent higher chance of being happy themselves. Furthermore, people at the center of a social network tend to become happier in the future than those at the periphery. Clusters of happy and unhappy people were discerned within the studied networks, with a reach of three degrees of separation: a person's happiness was associated with the level of happiness of their friends' friends' friends.[18] (See also Emotional contagion.)

Some researchers have suggested that human social networks may have a genetic basis.

National Longitudinal Study of Adolescent Health, they found that in-degree (the number of times a person is named as a friend), transitivity (the probability that two friends are friends with one another), and betweenness centrality (the number of paths in the network that pass through a given person) are all significantly heritable. Existing models of network formation cannot account for this intrinsic node variation, so the researchers propose an alternative "Attract and Introduce" model that can explain heritability and many other features of human social networks.[20]

Metrics (measures) in social network analysis

Betweenness centrality
The extent to which a node lies between other nodes in the network. This measure takes into account the connectivity of the node's neighbors, giving a higher value for nodes which bridge clusters. The measure reflects the number of people who a person is connecting indirectly through their direct links.[21]
Bridge
An edge is said to be a bridge if deleting it would cause its endpoints to lie in different components of a graph.
Centrality
This measure gives a rough indication of the social power of a node based on how well they "connect" the network. "Betweenness," "Closeness," and "Degree" are all measures of centrality.
Centralization
The difference between the number of links for each node divided by maximum possible sum of differences. A centralized network will have many of its links dispersed around one or a few nodes, while a decentralized network is one in which there is little variation between the number of links each node possesses.
Closeness
The degree an individual is near all other individuals in a network (directly or indirectly). It reflects the ability to access information through the "
grapevine" of network members. Thus, closeness is the inverse of the sum of the shortest distances between each individual and every other person in the network. (See also: Proxemics
) The shortest path may also be known as the "geodesic distance."
Clustering coefficient
A measure of the likelihood that two associates of a node are associates themselves. A higher clustering coefficient indicates a greater 'cliquishness.'
Cohesion
The degree to which actors are connected directly to each other by
social circles’ if there is less stringency of direct contact, which is imprecise, or as structurally cohesive blocks if precision is wanted.[22]
Degree
The count of the number of ties to other actors in the network. See also degree (graph theory).
(Individual-level) Density
The degree a respondent's ties know one another/ proportion of ties among an individual's nominees. Network or global-level density is the proportion of ties in a network relative to the total number possible (sparse versus dense networks).
Efficient immunization strategy
The acquaintance immunization strategy, propose to immunize friends of randomly selected nodes. It is found to be very efficient compared to random immunization.[23]
Flow betweenness centrality
The degree that a node contributes to sum of maximum flow between all pairs of nodes (not that node).
Eigenvector centrality
A measure of the importance of a
network
. It assigns relative scores to all nodes in the network based on the principle that connections to nodes having a high score contribute more to the score of the node in question.
Human interaction
Links in social networks are formed through human interactions. Scaling laws in human interaction activity were found by Rybski et al.[24]
Influential Spreaders
A method to identify influential spreaders is described by Kitsak et al.[25]
Local bridge
An edge is a local bridge if its endpoints share no common neighbors. Unlike a bridge, a local bridge is contained in a cycle.
Path length
The distances between pairs of nodes in the network. Average path-length is the average of these distances between all pairs of nodes.
Prestige
In a directed graph prestige is the term used to describe a node's centrality. "Degree Prestige," "Proximity Prestige," and "Status Prestige" are measures of Prestige. See also degree (graph theory).
Radiality
Degree an individual’s network reaches out into the network and provides novel information and influence.
Reach
The degree any member of a network can reach other members of the network.
Second order centrality
It assigns relative scores to all nodes in the network based on the observation that important nodes see a random walk (running on the network) "more regularly" than other nodes.[26]
Structural cohesion
The minimum number of members who, if removed from a group, would disconnect the group.[27] The relation between fragmentation (Structural cohesion) and percolation theory is discussed by Li et al.[28]
Structural equivalence
Refers to the extent to which nodes have a common set of linkages to other nodes in the system. The nodes don’t need to have any ties to each other to be structurally equivalent.
Structural hole
Static holes that can be strategically filled by connecting one or more links to link together other points. Linked to ideas of social capital: if you link to two people who are not linked you can control their communication.

Network analytic software

Main article: Social network analysis software

Network analytic tools are used to represent the nodes (agents) and edges (relationships) in a network, and to analyze the network data. Like other software tools, the data can be saved in external files. Additional information comparing the various data input formats used by network analysis software packages is available at NetWiki. Network analysis tools allow researchers to investigate large networks like the Internet, disease transmission, etc. These tools provide mathematical functions that can be applied to the network model.

Visualization of networks

Visual representation of social networks is important to understand the network data and convey the result of the analysis [1]. Many of the analytic software have modules for network visualization. Exploration of the data is done through displaying nodes and ties in various layouts, and attributing colors, size and other advanced properties to nodes. Visual representations of networks may be a powerful method for conveying complex information, but care should be taken in interpreting node and graph properties from visual displays alone, as they may misrepresent structural properties better captured through quantitative analyses.[29]

Typical representation of the network data are graphs in network layout (nodes and ties). These are not very easy-to-read and do not allow an intuitive interpretation. Various new methods have been developed in order to display network data in more intuitive format (e.g. Sociomapping).

Especially when using social network analysis as a tool for facilitating change, different approaches of participatory network mapping have proven useful. Here participants / interviewers provide network data by actually mapping out the network (with pen and paper or digitally) during the data collection session. One benefit of this approach is that it allows researchers to collect qualitative data and ask clarifying questions while the network data is collected.

Net-Map (pen-and-paper based) and VennMaker
(digital).

Patents

business method patents
.

See also

References

  1. ^ Linton Freeman, The Development of Social Network Analysis. Vancouver: Empirical Press, 2006.
  2. ^ Wellman, Barry and S.D. Berkowitz, eds., 1988. Social Structures: A Network Approach. Cambridge: Cambridge University Press.
  3. ^ Hansen, William B. and Reese, Eric L. 2009. Network Genie User Manual. Greensboro, NC: Tanglewood Research.
  4. ^ Freeman, Linton. 2006. The Development of Social Network Analysis. Vancouver: Empirical Pres, 2006; Wellman, Barry and S.D. Berkowitz, eds., 1988. Social Structures: A Network Approach. Cambridge: Cambridge University Press.
  5. ^ Scott, John. 1991. Social Network Analysis. London: Sage.
  6. ^ Wasserman, Stanley, and Faust, Katherine. 1994. Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press.
  7. ^ ShinoveneImmanuel, The Namibian 03 June, 2011
  8. ^ http://tech-wonders.blogspot.com/2011/05/basic-pros-and-cons-of-social.html
  9. ^ The Development of Social Network Analysis Vancouver: Empirical Press.
  10. ^ A.R. Radcliffe-Brown, "On Social Structure," Journal of the Royal Anthropological Institute: 70 (1940): 1–12.
  11. ^ Nadel, SF. 1957. The Theory of Social Structure. London: Cohen and West.
  12. ^ The Networked Individual: A Profile of Barry Wellman
  13. ^ 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.
  14. ^ 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. (see also Scott, 2000 and Freeman, 2004).
  15. ^ Barry Wellman, Wenhong Chen and Dong Weizhen. “Networking Guanxi." Pp. 221–41 in Social Connections in China: Institutions, Culture and the Changing Nature of Guanxi, edited by Thomas Gold, Douglas Guthrie and David Wank. Cambridge University Press, 2002.
  16. ^ Could It Be A Big World After All?: Judith Kleinfeld article.
  17. ^ Six Degrees: The Science of a Connected Age, Duncan Watts.
  18. British Medical Journal. December 4, 2008: doi:10.1136/bmj.a2338. Media account for those who cannot retrieve the original: Happiness: It Really is Contagious
    Retrieved December 5, 2008.
  19. ^ Shishkin, Philip (January 27, 2009). "Genes and the Friends You Make". Wall Street Journal.
  20. PMID 19171900
    .
  21. ^ The most comprehensive reference is: Wasserman, Stanley, & Faust, Katherine. (1994). Social Networks Analysis: Methods and Applications. Cambridge: Cambridge University Press. A short, clear basic summary is in Krebs, Valdis. (2000). "The Social Life of Routers." Internet Protocol Journal, 3 (December): 14–25.
  22. ^ Cohesive.blocking is the R program for computing structural cohesion according to the Moody-White (2003) algorithm. This wiki site provides numerous examples and a tutorial for use with R.
  23. ^ R. Cohen, S. Havlin, D. ben-Avraham (2003). "Efficient immunization strategies for computer networks and populations". Phys. Rev. Lett. 91: 247901.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  24. ^ D. Rybski, S. V. Buldyrev, S. Havlin, F. Liljeros, H. A. Makse (2009). "Scaling laws of human interaction activity". PNAS. 106: 12640.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  25. doi:10.1038/nphys1746.{{cite journal}}: CS1 maint: multiple names: authors list (link
    )
  26. ^ Second order centrality: Distributed assessment of nodes criticity in complex networks, Computer Communications, Volume 34, Issue 5, 15 April 2011, Pages 619-628
  27. PDF
    file).
  28. ^ Y. Chen,G. Paul, R. Cohen, S. Havlin, S. P. Borgatti, F. Liljeros, H. E. Stanley (2007). "Percolation theory applied to measures of fragmentation in social networks". Phys. Rev. E 75: 046107.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  29. ^ McGrath, Blythe and Krackhardt. 1997. "The effect of spatial arrangement on judgements and errors in interpreting graphs”. Social Networks 19: 223-242.
  30. ^ Bernie Hogan, Juan-Antonio Carrasco and Barry Wellman, "Visualizing Personal Networks: Working with Participant-Aided Sociograms," Field Methods 19 (2), May 2007: 116-144.
  31. ^ Mark Nowotarski, "Don't Steal My Avatar! Challenges of Social Network Patents, IP Watchdog, January 23, 2011.
  32. ^ USPTO search on published patent applications mentioning “social network”

Further reading

External links

Wikibooks has a book on the topic of: Social networking


[[Category:Social networks|*]] [[Category:Social psychology]] [[Category:Networks]] [[Category:Value]] [[Category:Systems theory]] [[Category:Social systems]] [[Category:Self-organization]] [[Category:Community building]] [[Category:Sociology]] [[Category:Cultural economics]] [[Category:Social information processing]] [[Category:Surveillance]]





--Tangeny (talk) 07:05, 26 September 2011 (UTC)

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