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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.

Signed 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.

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A balanced cycle is defined as a cycle where the product of all the signs are positive. According to balance theory , balanced graphs represent a group of people who are unlikely to change their opinions of the other people in the group.

Social Networking: Mining, Visualization, and Security - Google книги

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 balanced and unbalanced cycles, the evolution of signed social network graphs can be predicted. Especially when using social network analysis as a tool for facilitating change, different approaches of participatory network mapping have proven useful. One benefit of this approach is that it allows researchers to collect qualitative data and ask clarifying questions while the network data is collected.

Social Networking Potential SNP is a numeric coefficient , derived through algorithms [42] [43] to represent both the size of an individual's social network and their ability to influence that network. SNP coefficients were first defined and used by Bob Gerstley in By calculating the SNP of respondents and by targeting High SNP respondents, the strength and relevance of quantitative marketing research used to drive viral marketing strategies is enhanced.

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, See Viral Marketing. The first book [45] to discuss the commercial use of Alpha Users among mobile telecoms audiences was 3G Marketing by Ahonen, Kasper and Melkko in Social network analysis is used extensively in a wide range of applications and disciplines. Some common network analysis applications include data aggregation and mining , network propagation modeling, network modeling and sampling, user attribute and behavior analysis, community-maintained resource support, location-based interaction analysis, social sharing and filtering, recommender systems development, and link prediction and entity resolution.

Some public sector uses include development of leader engagement strategies, analysis of individual and group engagement and media use , and community-based problem solving. Social network analysis is also used in intelligence, counter-intelligence and law enforcement activities. This technique allows the analysts to map covert organizations such as a espionage ring, an organized crime family or a street gang. The National Security Agency NSA uses its clandestine mass electronic surveillance programs to generate the data needed to perform this type of analysis on terrorist cells and other networks deemed relevant to national security.

The NSA looks up to three nodes deep during this network analysis. The NSA has been performing social network analysis on call detail records CDRs , also known as metadata , since shortly after the September 11 attacks. Large textual corpora can be turned into networks and then analysed with the method of social network analysis. In these networks, the nodes are Social Actors, and the links are Actions. The extraction of these networks can be automated by using parsers. The resulting networks, which can contain thousands of nodes, are then analysed by using tools from network theory to identify the key actors, the key communities or parties, and general properties such as robustness or structural stability of the overall network, or centrality of certain nodes.

Social network analysis has also been applied to understanding online behavior by individuals, organizations, and between websites. Hyperlink analysis can be used to analyze the connections between websites or webpages to examine how information flows as individuals navigate the web. Social network analysis has been applied to social media as a tool to understand behavior between individuals or organizations through their linkages on social media websites such as Twitter and Facebook.

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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. It uses graphical representations, written representations, and data representations to help examine the connections within a CSCL network. The focus of the analysis is on the "connections" made among the participants — how they interact and communicate — as opposed to how each participant behaved on his or her own.

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. This particular method allows the study of interaction patterns within a networked learning community and can help illustrate the extent of the participants' interactions with the other members of the group. Some authors also suggest that SNA provides a method of easily analyzing changes in participatory patterns of members over time.

The findings include the correlation between a network's density and the teacher's presence, [59] a greater regard for the recommendations of "central" participants, [61] infrequency of cross-gender interaction in a network, [62] and the relatively small role played by an instructor in an asynchronous learning network.

Although many studies have demonstrated the value of social network analysis within the computer-supported collaborative learning field, [59] 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.

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. From Wikipedia, the free encyclopedia. This article is about the theoretical concept. For social networking sites, see social networking service.

For other uses, see Social network disambiguation.

Social Network Analysis with R - Examples

Metrics Algorithms. This section may require cleanup to meet Wikipedia's quality standards. The specific problem is: More careful cleanup after merge required Please help improve this section if you can. December Learn how and when to remove this template message. See also: Social network analysis criminology. Actor-network theory Community structure Complex network Digital humanities Dynamic network analysis Friendship paradox Individual mobility Mathematical sociology Metcalfe's law Network-based diffusion analysis Network science Organizational patterns Small world phenomenon Social media analytics Social media mining Social network Social network analysis software Social networking service Social software Social web Sociomapping.

Journal of Information Science. Retrieved Memoria e Ricerca 2 : — Social Network Analysis in Telecommunications. In Abraham, Ajith ed. Life Sciences Education.

BBC News. September 24, Retrieved July 25, Tech Crunch.

Mining, Visualization, and Security

J Sports Med and Phys Fitnes. The social network analysis was used to analyze properties of the network We-Sport. The development of social network analysis: a study in the sociology of science PDF. Vancouver, B.

The Development of Social Network Analysis. Vancouver: Empirical Press.

American Journal of Sociology. Annual Review of Sociology. American Sociological Review. Social networks and organisations. Sage Publications. Understanding social networks: Theories, concepts, and findings. Oxford: Oxford University Press. Transitivity, homophily, and the need for network closure". Journal of Personality and Social Psychology. Morgan Kaufmann. Social Networks Analysis: Methods and Applications. Cambridge: Cambridge University Press. A short, clear basic summary is in Krebs, Valdis Internet Protocol Journal. Social Networks. Biblio is a marketplace for book collectors comprised of thousands of independent, professional booksellers, located all over the world, who list their books for sale online so that customers like you can find them!

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