A social network is a social structure Social structure is a term used in the social sciences to refer to patterned social arrangements which form the society as a whole, and which determine, to some varying degree, the actions of the individuals socialised into that structure. Whereas 'structure' refers to "the macro", "agency" refers to "the micro" made of individuals (or organizations) called "nodes," which are tied (connected) by one or more specific types of interdependency, such as friendship In a comparison of personal relationships, friendship is considered to be closer than association, although there is a range of degrees of intimacy in both friendships and associations. Friendship and association can be thought of as spanning across the same continuum. The study of friendship is included in sociology, social psychology,, kinship Kinship is a relationship between any entities that share a genealogical origin, through either biological, cultural, or historical descent. In anthropology the kinship system includes people related both by descent and marriage, while usage in biology includes descent and mating. Human kinship relations through marriage are commonly called ", common interest, financial exchange, dislike, sexual relationships, or relationships of beliefs, knowledge or prestige Prestige is a word commonly used to describe reputation or esteem, though it has three somewhat related meanings that, to some degree, may be contradictory. Which meaning applies depends on the historical context and the person using the word.

Social network analysis views social relationships In social science, a social relation or social interaction refers to a relationship between two , three (i.e. a triad) or more individuals (e.g. a social group). Social relations, derived from individual agency, form the basis of the social structure. To this extent social relations are always the basic object of analysis for social scientists in terms of network theory Network theory is an area of computer science and network science and part of graph theory. It has application in many disciplines including particle physics, computer science, biology, economics, operations research, and sociology. Network theory concerns itself with the study of graphs as a representation of either symmetric relations or, more consisting of nodes and ties. Nodes A node is an abstract basic unit used to build linked data structures such as trees, linked lists, and computer-based representations of graphs. Each node contains some data and possibly links to other nodes. Links between nodes are often implemented by pointers or references are the individual actors within the networks, and ties are the relationships between the actors. The resulting graph In mathematics, a graph is an abstract representation of a set of objects where some pairs of the objects are connected by links. The interconnected objects are represented by mathematical abstractions called vertices, and the links that connect some pairs of vertices are called edges. Typically, a graph is depicted in diagrammatic form as a set-based structures are often very complex In the context of network theory, a complex network is a network with non-trivial topological features—features that do not occur in simple networks such as lattices or random graphs. The study of complex networks is a young and active area of scientific research inspired largely by the empirical study of real-world networks such as computer. 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 all of the relevant ties between all the nodes being studied. The network can also be used to measure social capital Social capital is a sociological concept, which refers to connections within and between social networks. Though there are a variety of related definitions, which have been described as "something of a cure-all" for the problems of modern society, they tend to share the core idea "that social networks have value. Just as a -- the value that an individual gets from the social network. These concepts are often displayed in a social network diagram, where nodes are the points and ties are the lines.

Contents

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 network theory Network theory is an area of computer science and network science and part of graph theory. It has application in many disciplines including particle physics, computer science, biology, economics, operations research, and sociology. Network theory concerns itself with the study of graphs as a representation of either symmetric relations or, more) has emerged as a key technique in modern sociology Sociology is the study of society. It is a social science—a term with which it is sometimes synonymous—that uses various methods of empirical investigation and critical analysis to develop and refine a body of knowledge about human social activity, often with the goal of applying such knowledge to the pursuit of social welfare. Subject matter. It has also gained a significant following in anthropology Anthropology is the study of humanity. Anthropology has origins in the natural sciences, the humanities, and social sciences. The term "anthropology", pronounced /ænθrɵˈpɒlədʒi/, is from the Greek ἄνθρωπος, anthrōpos, "human", and -λογία, -logia, "discourse" or "study", and was first, biology Biology is a natural science concerned with the study of life and living organisms, including their structure, function, growth, origin, evolution, distribution, and taxonomy, communication studies Communication studies is an academic field that deals with processes of communication, commonly defined as the sharing of symbols over distances in space and time. Hence, communication studies encompasses a wide range of topics and contexts ranging from face-to-face conversation to speeches to mass media outlets such as television broadcasting, economics Economics is the social science that is concerned with the production, distribution, and consumption of goods and services. The term economics comes from the Ancient Greek οἰκονομία from οἶκος (oikos, "house") + νόμος (nomos, "custom" or "law"), hence "rules of the house(hold)". Current, geography Geography is the study of the Earth and its lands, features, inhabitants, and phenomena. A literal translation would be "to describe or write about the Earth". The first person to use the word "geography" was Eratosthenes (276-194 B.C.). Four historical traditions in geographical research are the spatial analysis of natural and, information science Information science is an interdisciplinary science primarily concerned with the analysis, collection, classification, manipulation, storage, retrieval and dissemination of information. Practitioners within the field study the application and usage of knowledge in organizations, along with the interaction between people, organizations and any, organizational studies Organizational Behaviour studies encompasses the study of organizations from multiple viewpoints, methods, and levels of analysis. For instance, one textbook divides these multiple viewpoints into three perspectives: modern, symbolic, and postmodern. Another traditional distinction, present especially in American academia, is between the study of &, social psychology Social psychology is the study of the relations between people and groups. Scholars in this interdisciplinary area are typically either psychologists or sociologists, though all social psychologists employ both the individual and the group as their units of analysis, and sociolinguistics Sociolinguistics is the study of the effect of any and all aspects of society, including cultural norms, expectations, and context, on the way language is used, and the effects of language use on society. Sociolinguistics differs from sociology of language in that the focus of sociolinguistics is the effect of the society on the language, while, 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 groups In the social sciences a group can be defined as two or more humans who interact with one another, accept expectations and obligations as members of the group, and share a common identity. By this definition, society can be viewed as a large group, though most social groups are considerably smaller (e.g., tribes, families) and social categories Categorization is the process in which ideas and objects are recognized, differentiated and understood. Categorization implies that objects are grouped into categories, usually for some specific purpose. Ideally, a category illuminates a relationship between the subjects and objects of knowledge. Categorization is fundamental in language, (e.g., gender, ethnicity). Scholars such as S.D. Berkowitz, Stephen Borgatti, Ronald Burt Ronald S. Burt is the Hobart W. Williams Professor of Sociology and Strategy at the University of Chicago Booth School of Business. He is most notable for his research and writing on social networks and social capital, particularly the concept of structural holes in a social network, Kathleen Carley Kathleen M. Carley is an American social scientist specializing in dynamic network analysis. She is a professor in the School of Computer Science in the Institute for Software Research International at Carnegie Mellon University and also holds appointments in the Tepper School of Business, the Heinz College, the Department of Engineering and, Martin Everett, Katherine Faust, Linton Freeman, Mark Granovetter Mark Granovetter is an American sociologist who has created some of the most influential theories in modern sociology since the 1970s. He is best known for his work in social network theory and in economic sociology, particularly his theory on the spread of information in social networks known as "The Strength of Weak Ties", David Knoke, David Krackhardt, Peter Marsden, Nicholas Mullins, Anatol Rapoport Anatol Rapoport was a Russian-born American Jewish mathematical psychologist. He contributed to general systems theory, mathematical biology and to the mathematical modeling of social interaction and stochastic models of contagion, Stanley Wasserman, Barry Wellman Barry Wellman, FRSC directs NetLab as the S.D. Clark Professor of Sociology at the University of Toronto. His areas of research are community sociology, the Internet, human-computer interaction and social structure, as manifested in social networks in communities and organizations. His overarching interest is in the paradigm shift from group-, Douglas R. White Douglas R. White is an American complexity researcher , social anthropologist, sociologist, and social network researcher at the University of California, Irvine, and Harrison White Harrison Colyar White, born March 21, 1930, is the Giddings Professor of Sociology at Columbia University. White is an influential scholar in the domain of social networks. He is credited with the development of a number of mathematical models of social structure including vacancy chains and blockmodels. He has been a leader of a revolution in 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 Social network analysis software is used to identify, represent, analyze, visualize, or simulate nodes and edges (relationships) from various types of input data (relational and non-relational), including mathematical models of social networks. The output data can be saved in external files. Various input and output file formats exist, 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] 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 In biological terms, a community is a group of interacting species sharing an environment. In human communities, intent, belief, resources, preferences, needs, risks, and a number of other conditions may be present and common, affecting the identity of the participants and their degree of cohesiveness to links among websites A website is a collection of related web pages, images, videos or other digital assets that are addressed relative to a common Uniform Resource Locator (URL), often consisting of only the domain name, or the IP address, and the root path ('/') in an Internet Protocol-based network. A web site is hosted on at least one web server, accessible via a.
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 In mathematical sociology, interpersonal ties are defined as information-carrying connections between people. Interpersonal ties, generally, come in three varieties: strong, weak, or absent. Weak social ties, it is argued, are responsible for the majority of the embeddedness and structure of social networks in society as well as the transmission) 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 Collusion is an agreement, sometimes illegal and therefore secretive, which occurs between two or more persons to limit open competition by deceiving, misleading, or defrauding others of their legal rights, or to obtain an objective forbidden by law typically by defrauding or gaining an unfair advantage[citation needed]. It is an agreement among in setting prices or policies.[6]

History of social network analysis

A summary of the progress of social networks and social network analysis has been written by Linton Freeman.[7]

Precursors of social networks in the late 1800s include Émile Durkheim David Émile Durkheim (April 15, 1858 – November 15, 1917) was a French sociologist. He formally established the academic discipline and, with Karl Marx and Max Weber, is commonly cited as the principal architect of modern social science and Ferdinand Tönnies Ferdinand Tönnies (July 26, 1855, near Oldenswort - April 9, 1936, Kiel, Germany) was a German sociologist. He was a major contributor to sociological theory and field studies, as well as bringing Thomas Hobbes back on the agenda, by publishing his manuscripts. He is best known for his distinction between two types of social groups —. 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 Georg Simmel was one of the first generation of German sociologists. His neo-Kantian approach laid the foundations for sociological antipositivism, asking 'What is society?' in a direct allusion to Kant's question 'What is nature?', presenting pioneering analyses of social individuality and fragmentation. For Simmel, culture referred to "the, 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, J.L. Moreno Jacob Levy Moreno was an Austrian- American leading psychiatrist and psycho sociologist, thinker and educator, a Sefardic Jew born in Romania, the founder of psychodrama, and the foremost pioneer of group psychotherapy. During his lifetime, he was recognized as one of the leading social scientists pioneered the systematic recording and analysis of social interaction in small groups, especially classrooms and work groups (sociometry Sociometry is a quantitative method for measuring social relationships. It was developed by psychotherapist Jacob L. Moreno in his studies of the relationship between social structures and psychological well-being), while a Harvard Harvard University is a private university located in Cambridge, Massachusetts, and a member of the Ivy League. Established in 1636 by the colonial Massachusetts legislature, Harvard is the first corporation chartered in the United States and oldest institution of higher learning in the United States group led by W. Lloyd Warner William Lloyd Warner was a pioneering anthropologist noted for applying the techniques of his discipline to contemporary American culture and Elton Mayo George Elton Mayo was an Australian psychologist, sociologist and organization theorist explored interpersonal relations at work. In 1940, A.R. Radcliffe-Brown Alfred Reginald Radcliffe-Brown was an English social anthropologist who developed the theory of Structural Functionalism, a framework that describes basic concepts relating to the social structure of primitive civilizations's presidential address to British anthropologists urged the systematic study of networks.[8] 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 England The area now called England has been settled by people of various cultures for about 35,000 years, but it takes its name from the Angles, one of the Germanic tribes who settled during the 5th and 6th centuries. England became a unified state in AD 927, and since the Age of Discovery, which began during the 15th century, has had a significant in the 1950s and the 1950s-1960s urbanization Urbanization is the physical growth of urban areas as a result of global change. Urbanization is also defined by the United Nations as movement of people from rural to urban areas with population growth equating to urban migration. The United Nations projected that half of the world's population would live in urban areas at the end of 2008 studies of the University of Manchester The University of Manchester is a "red brick" civic university located in Manchester, England. It is a member of the Russell Group of large research-intensive universities and the N8 Group for research collaboration. The university was formed in 2004 by the dissolution of the Victoria University of Manchester and UMIST (University of group of anthropologists (centered around Max Gluckman Max Gluckman (26 January 1911 – 13 April 1975) was a South African-born British social anthropologist and later J. Clyde Mitchell James Clyde Mitchell (21 June 1918 – 15 November 1995) was a British sociologist and anthropologist) investigating community networks in southern Africa, India and the United Kingdom. Concomitantly, British anthropologist S.F. Nadel Siegfried Frederick Nadel , known as Fred Nadel, was an Austrian-born British anthropologist, specialising in African ethnology codified a theory of social structure that was influential in later network analysis.[9]

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 important in this early group were Charles Tilly, who focused on networks in political sociology and social movements, and Stanley Milgram, who developed the "six degrees of separation" thesis.[10] Mark Granovetter and Barry Wellman are among the former students of White who have elaborated and popularized social network analysis.[11]

White's was not the only group. Significant independent work was 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.[12]

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.

Diffusion of innovations theory explores social networks and their role in influencing the spread of new ideas and practices. Change agents and opinion leaders often play major roles in spurring the adoption of innovations, although factors inherent to the innovations also play a role.

Robin Dunbar has suggested that the typical size of an egocentric network is constrained to about 150 members due to possible limits in the capacity of the human communication channel. The rule arises from cross-cultural studies in sociology and especially anthropology of the maximum size of a village (in modern parlance most reasonably understood as an ecovillage). It is theorized in evolutionary psychology that the number may be some kind of limit of average human ability to recognize members and track emotional facts about all members of a group. However, it may be due to economics and the need to track "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 is a central concept in Chinese society (and other East Asian cultures) that can be summarized as the use of personal influence. Guanxi can be studied from a social network approach.[13]

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 was later questioned by an American scholar, and some further research to replicate Milgram's findings had found that the degrees of connection needed could be higher.[14] 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.[15]

Collaboration graphs can be used to illustrate good and bad relationships between humans. A positive edge between two nodes denotes a positive relationship (friendship, alliance, dating) and a negative edge between two nodes denotes a negative relationship (hatred, anger). Signed social network graphs can be used to predict the future evolution of the graph. In signed social networks, there is the concept of "balanced" and "unbalanced" cycles. A balanced cycle is defined as a cycle where the product of all the signs are positive. Balanced graphs represent a group of people who are unlikely to change their opinions of the other people in the group. Unbalanced graphs represent a group of people who are very likely to change their opinions of the people in their group. For example, a group of 3 people (A, B, and C) where A and B have a positive relationship, B and C have a positive relationship, but C and A have a negative relationship is an unbalanced cycle. This group is very likely to morph into a balanced cycle, such as one where B only has a good relationship with A, and both A and B have a negative relationship with C. By using the concept of balances and unbalanced cycles, the evolution of signed 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.[16]

Some researchers have suggested that human social networks may have a genetic basis.[17] Using a sample of twins from the 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.[18]

Application to Environmental Issues

The 1984 book The IRG Solution argued that central media and government-type hierarchical organizations could not adequately understand the environmental crisis we were manufacturing, or how to initiate adequate solutions. It argued that the widespread introduction of Information Routing Groups was required to create a social network whose overall intelligence could collectively understand the issues and devise and implement correct workeable solutions and policies.

Metrics (Measures) in social network analysis

Betweenness
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.[19]
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 cohesive bonds. Groups are identified as ‘cliques’ if every individual is directly tied to every other individual, ‘social circles’ if there is less stringency of direct contact, which is imprecise, or as structurally cohesive blocks if precision is wanted.[20]
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).
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 node in 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.
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 all 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.
Structural cohesion
The minimum number of members who, if removed from a group, would disconnect the group.[21]
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.

Vizualization of Networks

Visual representation of social networks is important to understand the network data and convey the result of the analysis [2]. Most of the softwares have besides the analytical tools also modules for network visuaization. Exploration of the data is done through displaying nodes and ties in various layouts, and attributing colors, size and other advanced properties to nodes.

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).

Patents

There has been rapid growth in the number of US patent applications that cover new technologies related to social networking. The number of published applications has been growing at about 250% per year over the past five years. There are now over 2000 published applications. [22] Only about 100 of these applications have issued as patents, however, largely due to the multi-year backlog in examination of business method patents and ethical issues connected with this patent category [23]

See also
Wikibooks has a book on the topic of Social networking

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. ^ The Development of Social Network Analysis Vancouver: Empirical Press.
  8. ^ A.R. Radcliffe-Brown, "On Social Structure," Journal of the Royal Anthropological Institute: 70 (1940): 1-12.
  9. ^ [Nadel, SF. 1957. The Theory of Social Structure. London: Cohen and West.
  10. ^ The Networked Individual: A Profile of Barry Wellman. [1]
  11. ^ 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.
  12. ^ 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).
  13. ^ 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.
  14. ^ Could It Be A Big World After All?: Judith Kleinfeld article.
  15. ^ Six Degrees: The Science of a Connected Age, Duncan Watts.
  16. ^ James H. Fowler and Nicholas A. Christakis. 2008. "Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study." British Medical Journal. December 4, 2008: doi:10.1136/bmj.a2338. Media account for those who cannot retrieve the original: Retrieved December 5, 2008.
  17. ^ "Genes and the Friends You Make". Wall Street Journal. January 27, 2009. http://online.wsj.com/article/SB123302040874118079.html.
  18. ^ Fowler, J. H. (10 February 2009). "Model of Genetic Variation in Human Social Networks" (PDF). Proceedings of the National Academy of Sciences 106 (6): 1720–1724. doi:10.1073/pnas.0806746106. http://jhfowler.ucsd.edu/genes_and_social_networks.pdf.
  19. ^ 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.
  20. ^ 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.
  21. ^ Moody, James, and Douglas R. White (2003). "Structural Cohesion and Embeddedness: A Hierarchical Concept of Social Groups." American Sociological Review 68(1):103-127. Online: (PDF file.
  22. ^ USPTO search on published patent applications mentioning “social network”
  23. ^ USPTO search on issued patents mentioning “social network”

Further reading

External links

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'Social Networks' News
CSC Launches New Social Network Platform with SelectMinds to Harness the Power ... - MarketWatch (press release)
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CSC Launches New Social Network Platform with SelectMinds to Harness the Power ... - MarketWatch (press release)
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Platform with SelectMinds to Harness the Power ... MarketWatch (press release) SelectMinds' industry-leading social networking platform enhances the portfolio of advanced social business software that CSC uses for enterprise and client ...
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add a comment There s a ton of chatter in the media today about MySpace Friendster

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'Social Networks' Blogs
IGN Launching Social Network for Gamers
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IGN Launching Social Network for Gamers

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Wed, 28 Jul 2010 23:12:02 GM

IGN is the worlds biggest gaming site and they are now launching MyIGN, a . social network. for gaming fans. Eventually, IGN wants to be able to offer instantaneous game demos or game purchases via digital distribution.

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'Social Networks' Answers
What are some good social networks to meet new friends/dating?
Q. I don t like paid dating sites - But I have tried Tagged and it is a mass meat market - I want something that is a little bit more up market and doesn t attract people blatantly selling sex Are there any social or free dating sites that are for professionals. I have also tried facebook and myspace but not impressed...
Asked by unknown - Tue Jun 23 06:38:54 2009 - - 4 Answers - 0 Comments

A. try looking on craiglist ^_^ or try
Answered by unknown - Tue Jun 23 06:40:33 2009

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