Social network analysis

1800s: Martineau · Tocqueville · Marx · Spencer · Le Bon · Ward · Pareto · Tönnies · Veblen · Simmel · Durkheim · Addams · Mead · Weber · Du Bois · Mannheim · Elias Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory.

[23] Beginning in the late 1990s, social network analysis experienced a further resurgence with work by sociologists, political scientists, economists, computer scientists, and physicists such as Duncan J. Watts, Albert-László Barabási, Peter Bearman, Nicholas A. Christakis, James H. Fowler, Mark Newman, Matthew Jackson, Jon Kleinberg, and others, developing and applying new models and methods, prompted in part by the emergence of new data available about online social networks as well as "digital traces" regarding face-to-face networks.

[24][25] Even in the study of literature, network analysis has been applied by Anheier, Gerhards and Romo,[26] Wouter De Nooy,[27] and Burgert Senekal.

[28] Indeed, social network analysis has found applications in various academic disciplines as well as practical contexts such as countering money laundering and terrorism.

Similarity can be defined by gender, race, age, occupation, educational achievement, status, values or any other salient characteristic.

It also includes the shortest route when a longer one is unfeasible due to a high risk of message distortion or delivery failure.

[41][42] Distance: The minimum number of ties required to connect two particular actors, as popularized by Stanley Milgram's small world experiment and the idea of 'six degrees of separation'.

The acronym "SNP" and some of the first algorithms developed to quantify an individual's social networking potential were described in the white paper "Advertising Research is Changing" (Gerstley, 2003) See Viral Marketing.

[57] The first book[58] to discuss the commercial use of Alpha Users among mobile telecoms audiences was 3G Marketing by Ahonen, Kasper and Melkko in 2004.

The first book to discuss Alpha Users more generally in the context of social marketing intelligence was Communities Dominate Brands by Ahonen & Moore in 2005.

Similarity in characteristics, attitudes, or behaviors means that friends understand each other more quickly, have common interests to talk about, know better where they stand with each other, and have more trust in each other.

[65] Longitudinal social network analysis became mainstream after the publication of a special issue of the Journal of Research on Adolescence in 2013, edited by René Veenstra and containing 15 empirical papers.

[68] This allows military or law enforcement assets to launch capture-or-kill decapitation attacks on the high-value targets in leadership positions to disrupt the functioning of the network.

The NSA has been performing social network analysis on call detail records (CDRs), also known as metadata, since shortly after the September 11 attacks.

In these networks, nodes are words and links among them are weighted based on their frequency of co-occurrence (within a specific maximum range).

Social network analysis has also been applied to understanding online behavior by individuals, organizations, and between websites.

[17] Hyperlink analysis can be used to analyze the connections between websites or webpages to examine how information flows as individuals navigate the web.

When applied to CSCL, SNA is used to help understand how learners collaborate in terms of amount, frequency, and length, as well as the quality, topic, and strategies of communication.

There are several key terms associated with social network analysis research in computer-supported collaborative learning such as: density, centrality, indegree, outdegree, and sociogram.

Researchers employ social network analysis in the study of computer-supported collaborative learning in part due to the unique capabilities it offers.

Some authors also suggest that SNA provides a method of easily analyzing changes in participatory patterns of members over time.

[82] Although many studies have demonstrated the value of social network analysis within the computer-supported collaborative learning field,[78] researchers have suggested that SNA by itself is not enough for achieving a full understanding of CSCL.

The complexity of the interaction processes and the myriad sources of data make it difficult for SNA to provide an in-depth analysis of CSCL.

[83] Researchers indicate that SNA needs to be complemented with other methods of analysis to form a more accurate picture of collaborative learning experiences.

This can be referred to as a multi-method approach or data triangulation, which will lead to an increase of evaluation reliability in CSCL studies.

A social network diagram displaying friendship ties among a set of Facebook users.
Different characteristics of social networks. A, B, and C show varying centrality and density of networks; panel D shows network closure, i.e., when two actors, tied to a common third actor, tend to also form a direct tie between them. Panel E represents two actors with different attributes (e.g., organizational affiliation, beliefs, gender, education) who tend to form ties. Panel F consists of two types of ties: friendship (solid line) and dislike (dashed line). In this case, two actors being friends both dislike a common third (or, similarly, two actors that dislike a common third tend to be friends).
Narrative network of US Elections 2012 [ 73 ]