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A social network is a social structure made up of a set of actors (such as individuals or organizations) and the dyadic ties between these actors. The social network perspective provides a clear way of analyzing the structure of whole social entities.^[1] The study of these structures uses methods of 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 interdisciplinary academic field which emerged from social psychology, sociology, statistics, and graph theory. Georg Simmel authored early structural theories in sociology emphasizing the dynamics of triads and "web of group affiliations."^[2] Jacob Moreno is credited with developing the first sociograms in the 1930s to study interpersonal relationships as structures in which people were points and the relationships between them were drawn as connecting lines. These approaches were mathematically formalized in the 1950s and theories and methods of social networks became pervasive in the social and behavioral sciences by the 1980s.^[1][3] Social network analysis is now one of the major paradigms in contemporary sociology, and is also employed in a number of other social and formal sciences. Together with other complex networks, it forms part of the nascent field of network science.^[4][5]

Overview[link]

Evolution graph of a social network: Barabási model.

A social network is a theoretical construct useful in the social sciences to study relationships between individuals, groups, organizations, or even entire societies (social units, see differentiation). The term is used to describe a social structure determined by such interactions. The ties through which any given social unit connects represent the convergence of the various social contacts of that unit. This theoretical approach is, necessarily, relational. An axiom of the social network approach to understanding social interaction is that social phenomena should be primarily conceived and investigated through the properties of relations between and within units, instead of the properties of these units themselves. Thus, one common criticism of social network theory is that individual agency is often ignored,^[6] although this may not be the case in practice (see agent-based modeling). Precisely because many different types of relations, singular or in combination, form into network configurations, network analytics are useful to a broad range of research enterprises. In social science, these fields of study include, but are not limited to anthropology, biology, communication studies, economics, geography, information science, organizational studies, social psychology, sociology, and sociolinguistics. Scholars in these and other areas have used the idea of "social network" loosely for almost a century to connote complex sets of relationships between members of social units across all scales of analysis, from the local to the global as well as the scale-free.

Background[link]

Some of the ideas of social network theory are found in writings going back to the ancient Greeks. In the late 1800s, both Émile Durkheim and Ferdinand Tönnies foreshadow the idea of social networks in their theories and research 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, German, commonly translated as "community") or impersonal, formal, and instrumental social links (Gesellschaft, German, commonly translated as "society").^[7] Durkheim gave a non-individualistic explanation of social facts arguing that social phenomena arise when interacting individuals constitute a reality that can no longer be accounted for in terms of the properties of individual actors.^[8] Georg Simmel, writing at the turn of the twentieth century, pointed to the nature of networks and the effect of network size on interaction and examined the likelihood of interaction in loosely-knit networks rather than groups.^[9]

Major developments in the field can be seen in the 1930s by several groups in psychology, anthropology, and mathematics working independently.^[6] 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,^[10] Alfred Radcliffe-Brown,^[11] and Claude Lévi-Strauss.^[12] A group of social anthropologists associated with Max Gluckman and the Manchester School, including John A. Barnes,^[13] J. Clyde Mitchell and Elizabeth Bott Spillius,^[14][15] often are credited with performing some of the first fieldwork from which network analyses were performed.^[6] In sociology, the early (1930s) work of Talcott Parsons set the stage for taking a relational approach to understanding social structure.^[16][17] 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.^[18][19][20] 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. Mark Granovetter^[21] and Barry Wellman^[22] are among the former students of White who elaborated and championed the analysis of social networks.

Levels of analysis[link]

Self-organization of a network, based on Nagler, Levina, & Timme, (2011)^[23]

In general, social networks are self-organizing, emergent, and complex, such that a globally coherent pattern appears from the local interaction of the elements that make up the system.^[24][25] 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. Many real social systems, such as networks of firms in a field or strong social ties within a single school, also have natural bounds that would not make these enormous scales worthwhile, and the quality of information may be more important than its scale for understanding their 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.

This section requires expansion with: additional examples and references for each sub-level.

Micro level[link]

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.

Social network diagram, micro-level.

Actor level[link]

The smallest unit of analysis in a social network is an individual in their social setting, i.e., an "actor". Actor-centered network analysis often centers on network characteristics such as centrality, prestige and roles such as isolates, liaisons, and bridges. Such analyses, sometimes referred to as ego-centric or ego networks, are most commonly used in the fields of psychology or social pyschology, ethnographic kinship analysis or other genealogical studies of relationships between individuals.

Dyadic level[link]

Simply put, a dyad is a social relationship between two individuals. Network research on dyads may concentrate on structure of the relationship, social equality, and tendencies toward reciprocity.

Triadic level[link]

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.

Subset level[link]

Subset levels of network research problems begin at the micro-level, but may crossover into the meso-level of analysis. Subset level research may focus on distance and reachability, cliques, cohesive subgroups, or other group action, group actions or behavior.

Meso level[link]

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.^[26]

Social network diagram, meso-level

Organizations[link]

Formal organizations are social groups that distribute tasks for a collective goal. There are a variety of legal types of organizations, including: corporations, governments, non-governmental organizations, international organizations, armed forces, charities, not-for-profit corporations, partnerships, cooperatives, and universities. A hybrid organization is a body that operates in both the public sector and the private sector, simultaneously fulfilling public duties and developing commercial market activities. As a result the hybrid organization becomes a mixture of a government and a corporate organization. Network research on organizations may focus on either intra-organizational or inter-organizational ties in terms of formal or informal relationships.

Randomly-distributed networks[link]

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.^[27]

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

Scale-free networks[link]

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.^[28] 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.^[29]

The Barabási model of network evolution shown above is an example of a scale-free network.

Macro level[link]

Rather than tracing interpersonal interactions, macro-level analyses generally trace the outcomes of interactions, such as economic or other resource transfer interactions over a large population.

Diagram: section of a large-scale social network

Large-scale networks[link]

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).

Complex networks[link]

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.^[30]

Theory clusters[link]

This section requires expansion with: additional theoretical perspectives and additional examples and references for existing areas of theory.

Communications[link]

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.

Community[link]

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.

Complex Networks[link]

Complex networks require methods specific to modelling and interpreting social complexity and complex adaptive systems, including techniques of dynamic network analysis.

Criminal networks[link]

In criminology and urban sociology, much attention has been paid to the social networks among criminal actors. For example, Andrew Papachristos^{[citation needed]} 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.

Diffusion of innovations[link]

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.

Demography[link]

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.

Economic sociology[link]

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.^[31]

Health care[link]

Analysis of social networks is increasingly incorporated into heath care analytics, not only in epidemological studies but also in models of patient communication and education, disease prevention, mental health diagnosis and treatment, and in the study of health care organizations and systems.^[32]

Human ecology[link]

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.^[33][34]

Language/Linguistics[link]

Studies of language and lingustics, 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.

Organizational Studies[link]

Research studies of formal or informal organizational relationships, organizational communication, economics, economic sociology, and other resource transfers.

Social capital[link]

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 lack of knowledge of language). Studies show that there a positive relationship between social capital and the intensity of social network use.^[35]

Structural Holes[link]

Structural holes refer to the absence of ties between two parts of a network. Finding and exploiting a structural hole can give an entrepreneur a competitive advantage. For example, a unique combination of business ties can allow them to combine expertise from two otherwise disconnected fields to create novel products. They can also act as brokers, reaping a reward from mediating trade between the communities. This concept was developed by sociologist Ronald Burt, and is sometimes referred to as an alternate conception of social capital (above).

See also[link]

• Social relation
• Interpersonal relationship
• Social group
• Social network analysis
• Semiotics of social networking
• Social media
• Social networking
• Social web
• Network society
• Social complexity
• Complex networks
• Dynamic network analysis
• Network theory
• Network science

References[link]

Further reading[link]

• Wellman, Barry; Berkowitz, S.D. (1988). Social Structures: A Network Approach. Structural Analysis in the Social Sciences. Cambridge University Press. ISBN 0-521-24441-2. 
• Scott, John (1991). Social Network Analysis: a handbook. SAGE. ISBN 978-0-7619-6338-7. 
• Wasserman, Stanley; Faust, Katherine (1994). Social Network Analysis: Methods and Applications. Structural Analysis in the Social Sciences. Cambridge University Press. ISBN 978-0-521-38269-4. 
• Barabási, Albert-László (2003). Linked: How everything is connected to everything else and what it means for business, science, and everyday life. Plum. ISBN 978-0-452-28439-5. 
• Freeman, Linton C. (2004). The Development of Social Network Analysis: A Study in the Sociology of Science. Empirical Press. ISBN 1-59457-714-5. 
• Barnett, George A. (2011). Encyclopedia of Social Networks. SAGE. ISBN 978-1-4129-7911-5. 
• Kadushin, Charles (2012). Understanding Social Networks: Theories, Concepts, and Findings. Oxford University Press. ISBN 978-0-19-537946-4. 

External links[link]

Organizations[link]

• International Network for Social Network Analysis

Peer-reviewed journals[link]

• Social Networks
• Network Science
• Journal of Social Structure
• Journal of Mathematical Sociology

Textbooks and educational resources[link]

• 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

Data sets[link]

• 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

This biographical article needs additional citations for verification. Please help by adding reliable sources. Contentious material about living persons that is unsourced or poorly sourced must be removed immediately, especially if potentially libelous or harmful. (August 2011)

Seth Godin
Born	(1960-07-10) July 10, 1960 (age 51)
Alma mater	Tufts University Stanford University
Occupation	author, marketing guru
Website
sethgodin.com

Seth Godin (born July 10, 1960) is an American entrepreneur, author and public speaker. Godin popularized the topic of permission marketing.

Background[link]

Born in Mount Vernon, New York, Seth Godin graduated from Tufts University in 1979 with a degree in computer science and philosophy. Godin earned his MBA from the Stanford Graduate School of Business. From 1983 to 1986, he worked as a brand manager at Spinnaker Software. For a time Godin commuted every week between California and Boston both to do his new job and to complete his MBA..

After leaving Spinnaker Software in 1986, Godin used $20,000 in savings to found Seth Godin Productions, primarily a book packaging business, out of a studio apartment in New York City.^[1] It was in the same offices that Godin met Mark Hurst and founded Yoyodyne. After a few years Godin sold the book packaging business to his employees and focused his efforts on Yoyodyne, one of the first online marketing companies.^{[citation needed]} It was with Yoyodyne that Godin came up with the concept of permission marketing.^{[citation needed]} For a period of time, Godin served as a columnist for Fast Company^{[citation needed]}

Godin and his wife Helene now live in Hastings-on-Hudson, New York with their two sons.

Viewpoints[link]

Godin believes that the end of the "TV-Industrial complex" means that marketers no longer have the power to command the attention of anyone they choose, whenever they choose. Second, in a marketplace in which consumers have more power, he thinks marketers must show more respect; this means no spam, no deceit and a bias for keeping promises. Finally, Godin asserts that the only way to spread the word about an idea is for that idea to earn the buzz by being remarkable. Godin refers to those who spread these ideas as "Sneezers", and to the spreading idea as an "IdeaVirus." He calls a remarkable product or service a purple cow.

Advertisements on television and radio are classified as 'interruption marketing' which interrupt the customer while they are doing something of their preference. Godin introduced the concept of "permission marketing" where the business provides something "anticipated, personal, and relevant".

Business ventures[link]

Yoyodyne[link]

In 1995, Godin launched Yoyodyne which used contests, online games, and scavenger hunts to market companies to participating users. In August, 1996, venture-capital firm Flatiron Partners invested $4 million in Yoyodyne in return for a 20% stake.^[2] The site gained significant traction, with over one million viewers visiting the site, and companies like America Online, American Express, H&R Block, Microsoft, Procter & Gamble, Sony Music, Sprint, and Volvo using its services.^[3]

At Yoyodyne, Godin developed the principle of permission marketing and authored Permission Marketing: Turning strangers into friends and friends into customers.

In 1998, Godin sold Yoyodyne to Yahoo! for $30 million^[4] and became Yahoo's vice president of direct marketing, a position he held until 2000.^[5]

Squidoo[link]

In March 2006, Godin launched Squidoo, a community website allowing users to create pages (called "lenses") for subjects of interest.^[6] The site donates 5% of the profits to charity, and 50% to the lensmasters. Godin and Squidoo have been profiled on CNN and the Washington Post.^[7][8] The site was given top prize in SXSW's community/wiki category.^[9] As of July 2008, Squidoo was one of the 500 most visited sites in the world.^[10]

Other projects[link]

ChangeThis[link]

Godin developed the idea for ChangeThis, a website aimed at spreading ideas through PDF files.^[11] In the summer of 2004, Godin hired five interns—Amit Gupta, Catherine Hickey, Noah Weiss, Phoebe Espiritu and Michelle Sriwongtong—to build and develop the website.^[12] The website went live on August 14, 2004.^[13] Tom Peters, Chris Anderson, and Guy Kawasaki all had manifestos featured on ChangeThis.^[14] In July 2005, ChangeThis was turned over to 800-CEO-READ, the leading distributor of business literature in the United States.^[15][16]

Six month alternative MBA program[link]

In December 2008, Godin announced in a blog post that he would be offering a six month alternative MBA program at his office in Hastings on Hudson, NY.^[17] 48,000 people looked at the post and 340 applied. He invited 27 applicants to his office for a group interview. They spent two hours interviewing one another. After co-mingling, they and Godin together wrote down the names of their favorite candidates. Three weeks later the chosen 9 showed up at Godin's office.^[18] This group graduated in July 2009.^[19]

The Domino Project[link]

In December 2010, Godin announced on his blog that he would be working directly with Amazon on a venture called The Domino Project.^[20]

Stop Stealing Dreams (what is school for)[link]

In February 2012, Godin released a 30,000 word manifesto on Squidoo in response to the question "What do you think we ought to do about education?". This manifesto is "totally free to read, share, translate, print and, most of all, use to start an essential conversation". ^[21]

Books[link]

Godin is the author of 11 books; his Free Prize Inside was a Forbes Business Book of the Year in 2004,^[22] in its first two years of release, Purple Cow sold over 150,000 copies in more than 23 printings.^[23] The Dip was a Business Week and New York Times bestseller.^[24][25] And Godin has called his free ebook Unleashing the Ideavirus the most "popular ebook ever written".^[26] In the early 1990s he created a ten book series for children titled Worlds of Power, which was written by various writers. In each the plot of a single video game was told in a novelized form.^[27]

Beginning with Permission Marketing, Godin uses the concepts discussed in the books to promote the book. For Permission Marketing, Godin gave 1/3 of the book away for free to anyone who sent an e-mail. For Unleashing the Ideavirus, Godin released the entire eBook on the Internet for free, which led to eventual publishing deals in 41 countries and a public speaking career. For Purple Cow, Godin created a milk carton container for the book which generated attention from work colleagues. For Tribes, Godin launched an exclusive online community for the first 3000 people who pre-ordered the book. For Linchpin, Godin gave the book away for three weeks before its release for free to anyone willing to give $30 to the Acumen Fund for the $20 book, and raised $100,000 for the Acumen Fund.

Bibliography[link]

• Godin, Seth (1993). The Smiley Dictionary. Berkeley: Peachpit Press. ISBN 1-56609-008-3. 
• Godin, Seth (1995). eMarketing: Reaping Profits on the Information Highway. New York: Berkley Pub. Group. ISBN 0-399-51904-1. 
• Godin, Seth (1999). Permission marketing: turning strangers into friends, and friends into customers. New York: Simon & Schuster. ISBN 0-684-85636-0. 
• Godin, Seth (2001). Unleashing the Ideavirus. New York: Hyperion. ISBN 0-7868-8717-6.  Book online - Detailing the idea of Viral marketing
• Godin, Seth (2002). The Big Red Fez: How To Make Any Web Site Better. New York: Free Press. pp. 112. ISBN 0-7432-2790-5. 
• Godin, Seth (2002). Survival is not enough: zooming, evolution, and the future of your company. New York: Free Press. ISBN 0-7432-2571-6. 
• Godin, Seth (2003). Purple Cow: Transform Your Business by Being Remarkable. Portfolio. pp. 224. ISBN 1-59184-021-X. 
• Godin, Seth (2004). Free Prize Inside!: The Next Big Marketing Idea. Penguin USA Portfolio. pp. 256. ISBN 1-59184-041-4. 
• Godin, Seth (2005). All Marketers Are Liars: The Power of Telling Authentic Stories in a Low-Trust World. Portfolio Hardcover. ISBN 1-59184-100-3. 
• Godin, Seth; the Group of 33 (2005). The Big Moo: Stop Trying to be Perfect and Start Being Remarkable. Portfolio Hardcover. ISBN 1-59184-103-8. 
• Godin, Seth (2006). Small Is the New Big: and 193 Other Riffs, Rants, and Remarkable Business Ideas. Portfolio Hardcover. ISBN 1-59184-126-7. 
• Godin, Seth (2007). The Dip: A Little Book That Teaches You When to Quit (and When to Stick). Portfolio Hardcover. ISBN 1-59184-166-6. 
• Godin, Seth (2008). Meatball Sundae: Is Your Marketing out of Sync?. Portfolio Hardcover. ISBN 1-59184-174-7. 
• Godin, Seth (2008). Tribes: We Need You to Lead Us. Portfolio Hardcover. p. 160. ISBN 1-59184-233-6. 
• Godin, Seth (2010). Linchpin: Are You Indispensable?. Portfolio Hardcover. p. 256. ISBN 1-59184-316-2. 
• Godin, Seth (2011). Poke the Box. Portfolio Hardcover. ISBN 1-936719-00-2. 
• Godin, Seth (2011). We Are All Weird. The Domino Project. ISBN 1-936719-22-3. 

Blog[link]

Godin's Seth's Blog^[28] is ranked in the AdAge Power 150 as the #1 marketing blog out of 976 tracked. ^[29]

References[link]

External links[link]

• Official website
• Seth Godin on TED
• Seth Godin at the Internet Movie Database
• Subvert Magazine interview
• Smart People Podcast - interview (2011)
• The Marketplace of Ideas - interview (2010)