Social network analysis ( SNA ) is the process of investigating social structure through network usage and graph theory. It characterizes the network structure in terms of nodes (individual actors, people, or things in the network) and ties , edges , or links (relationships or interactions) that connect them. Examples of social structures commonly visualized through social network analysis include social media networks, scattered memes, friendship and contact networks, collaboration graphs, kinship, disease transmission, and sexual relationships. These networks are often visualized through sociograms where nodes are represented as dots and bonds are represented as lines.
Social networking analysis has emerged as a key technique in modern sociology. It also gained significant followers in anthropology, biology, demography, communication studies, economics, geography, history, information science, organizational studies, political science, social psychology, development studies, sociolinguistics, and computer science and is now generally available as a consumer tool (see SNA software list).
Video Social network analysis
History
Social network analysis has its theoretical roots in earlier sociologists such as Georg Simmel and ÃÆ'â € mile Durkheim, who wrote about the importance of studying the patterns of relationships that connect social actors. Social scientists have been using the concept of "social networking" since the beginning of the 20th century to connote a set of complex relationships between members of social systems at all scales, from interpersonal to international. In the 1930s, Jacob Moreno and Helen Jennings introduced basic analytical methods. In 1954, John Arundel Barnes began using the term systematically to denote bonding patterns, including concepts traditionally used by the public and those used by social scientists: restricted groups (eg, tribes, families) and social categories (eg, gender , ethnicity). Scholars such as Ronald Burt, Kathleen Carley, Mark Granovetter, David Krackhardt, Edward Laumann, Anatol Rapoport, Barry Wellman, Douglas R. White, and Harrison White expanded the use of systematic social network analysis. Even in the literature study, network analysis has been applied by Anheier, Gerhards and Romo, Wouter De Nooy, and Burgert Senekal. Indeed, social networking analysis has found applications in various disciplines, as well as practical applications such as against money laundering and terrorism.
Maps Social network analysis
Metrics
Connection
Homophily: The extent to which actors form bonds with others who are similar and different. Equality can be defined by gender, race, age, occupation, educational achievement, status, value or other prominent characteristics. Homofili is also called assortativity.
Multiplexity: The amount of content-form contained in a tie. For example, two people who are friends and also work together will have a multiplexity of 2. Multiplexity has been linked to the strength of the relationship.
Mutuality/Reciprocity: The extent to which two actors reply to each other's friendships or other interactions.
Network Closure: Relational triad completeness measure. Individual assumptions about network closure (ie their friends are also friends) are called transitivity. Transitivity is the result of the individual or situational nature of the Need for Cognitive Closure.
Propinquity: The tendency of actors to have more ties with geographically close people.
Distribution
Bridge: A person with a weak bond fills a structural hole, providing the only link between two individuals or groups. It also includes the shortest route when a longer one is not feasible due to the high risk of message distortion or delivery failure.
Centrality: Centrality refers to a set of metrics that aim to measure the "importance" or "influence" (in various senses) of a particular node (or group) within a network. Examples of common methods for measuring "centrality" include centrality betweenness, centrality of attachment, eigenvector centralization, alpha centrality, and centrality.
Density: The proportion of direct bonds in a network relative to the total possible amount.
Distance: The minimum number of ties required to connect two particular actors, as popularized by Stanley Milgram's small world experiment and the notion of 'six degrees of separation'.
Structural hole: Absence of connection between two parts of the network. Finding and exploiting structural holes can give entrepreneurs a competitive advantage. This concept was developed by the sociologist Ronald Burt, and is sometimes referred to as an alternative conception of social capital.
Tie Strength: Defined by a linear combination of time, emotional intensity, intimacy and reciprocity (ie mutuality). Strong ties are related to homophili, proximity and transitivity, while weak bonds are associated with bridges.
Segmentation
Groups are identified as 'gangs' if each individual is directly linked to any other individual, 'social circle' if there is little direct contact, which is not appropriate, or as a structurally cohesive bloc if precision is desired.
The grouping coefficient: The probable measure that two counterparts of a node are peers. Higher grouping coefficients show greater 'cliquishness'.
Cohesion: The extent to which actors connect directly to each other by cohesive bonds. Structural cohesion refers to the minimum number of members who, if removed from the group, will decide the group.
Modeling and visualizing a network
Visual representation of social networks is important to understand network data and deliver results of analysis. Many visualization methods for data generated by social network analysis have been presented. Many analytical software have modules for network visualization. Exploration of data is done through displaying nodes and bonds in various layouts, and connecting colors, sizes and other advanced properties to nodes. The visual representation of the network can be a powerful method for conveying complex information, but care must be taken in interpreting the node and graphic properties of the visual display only, as it may misrepresent the structural properties better captured through quantitative analysis.
The signed graph can be used to describe the good and bad relationships between people. The positive edge between the two nodes indicates a positive relationship (friendship, alliance, date) and the negative end between two nodes denotes a negative relationship (hatred, anger). A signed social network graph can be used to predict the evolution of graphs in the future. In signed social networks, there is a concept of "balanced" and "unbalanced" cycles. A balanced cycle is defined as the cycle in which the product of all signs is positive. According to the balance theory, a balanced graph represents a group of people who can not possibly change their opinions about others in the group. The unbalanced graph represents a group of people who are very likely to change their opinions about 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 turn into a balanced cycle, as where B only has a good relationship with A, and both A and B have a negative relationship with C. Using the concept of balanced and unbalanced cycles, the evolution of signed social network graphics can be predicted.
Particularly when using social network analysis as a tool to facilitate change, different participatory mapping approaches have proven useful. Here participants/interviewers provide network data by actually mapping the network (with pen and paper or digital) during the data collection session. An example of a networking approach of pen and paper, which also includes the collection of several actor attributes (the effect and perceived goals of the actor) is the * Net-map toolbox. One benefit of this approach is to enable researchers to collect qualitative data and ask clarification questions when network data is collected.
Potential of social network
The potential of social networking (SNP) is a numerical coefficient, which is derived through an algorithm to represent the size of an individual's social network and its ability to influence that network. Close synonym is Alpha User, someone with high SNP.
SNP coefficients have two main functions:
- the classification of individuals based on their social networking potential, and
- weighting of respondents in quantitative marketing research research.
By calculating the respondent's SNP and by targeting the High SNP respondent, the strength and relevance of the quantitative marketing research used to drive the viral marketing strategy is enhanced.
Variables used to calculate individual SNPs include but are not limited to: participation in Social Networking activities, group membership, leadership roles, acknowledgments, publications/edits/contributions to non-electronic media, publications/edits/contributions to electronic media (websites, blog), and the frequency of information distribution in the past in their network. The acronym "SNP" and some of the first algorithms developed to measure the potential of one's social networking are described in the white paper "Advertising Research is Changing" (Gerstley, 2003) See Viral Marketing.
The first book to discuss the commercial use of Alpha Users among mobile audiences was 3G Marketing by Ahonen, Kasper and Melkko in 2004. The first book that discusses Alpha Users more generally in the context of social marketing intelligence is the Community Dominating Brands by Ahonen & ; Moore in 2005. In 2012, Nicola Greco (UCL) is present at TEDx The Potential of Social Networking as a parallelism with potential energy generated by users and companies should be used, stating that "SNP is a new asset that every company should have".
Practical apps
Social networking analysis is widely used in a variety of applications and disciplines. Some common network analysis applications include data collection and mining, network modeling propagation, network modeling and sampling, user attributes and behavioral analysis, community-supported resource support, location-based interaction analysis, social sharing and sharing, system development recommendations, and predictions link and entity resolution. In the private sector, businesses use social network analysis to support activities such as customer interaction and analysis, analysis of information systems development, marketing, and business intelligence needs. Some uses of the public sector include the development of leadership engagement strategies, the analysis of individual and group engagement and media use, and community-based problem solving.
Security app
Social network analysis is also used in intelligence, counter-intelligence and law enforcement activities. This technique allows analysts to map a secret or secret organization such as an espionage ring, an organized crime family or street gang. The National Security Agency (NSA) uses its mass electronic surveillance program in secret to generate the data necessary to perform this type of analysis on terrorist cells and other networks deemed relevant to national security. NSA seen up to three deep nodes during this network analysis. After the initial mapping of the social network is complete, an analysis is performed to determine the network structure and determine, for example, leaders within the network. This allows military or law enforcement assets to initiate a catch-or-kill capture attack on high-value targets in leadership positions to disrupt the functioning of the network. The NSA has conducted a social network analysis on call detail records (CDR), also known as metadata, since shortly after the September 11 attacks.
Textual analysis app
Large textual corporations can be transformed into networks and then analyzed by social network analysis methods. In this network, the knots are Social Actors, and the link is Action. Extraction from this network can be automated, using a parser. The resulting network, which can contain thousands of nodes, is then analyzed using tools from network theory to identify key actors, communities or major parties, and common properties such as the robustness or structural stability of the entire network, or the centrality of a particular node. This automates the approach introduced by Quantitative Narrative Analysis, in which the subject-verbs of the triplets identified with actor pairs are connected by an action, or a pair formed by the object-actor.
Internet applications
Social networking analysis has also been applied to understand online behavior by individuals, organizations, and between websites. Hyperlink analysis can be used to analyze the relationship between a website or a web page to examine how information flows as individuals navigate the web. Inter-organizational connections have been analyzed through hyperlink analysis to examine which organizations are in the problem community.
In computer-supported collaborative learning
One of the latest methods of SNA applications is to study computer-supported collaborative learning (CSCL). When applied to CSCL, SNA is used to help understand how learners collaborate in terms of number, frequency, and length, as well as quality, topics, and communication strategies. In addition, SNA can focus on certain aspects of the network connection, or the entire network as a whole. It uses graphical representation, written representation, and data representation to help check connections within CSCL networks. When applying SNA to a CSCL environment the interaction of the participants is treated as a social network. The focus of analysis is on the "connections" made between the participants - how they interact and communicate - as opposed to how each participant behaves on his own.
Key terms
There are several key terms related to social network analysis research in computer-supported collaborative learning such as: density , centrality , indegree , outdegree , and sociogram .
- Density refers to "connections" between participants. Density is defined as the number of connections a participant has, divided by the possibility of a participant's connection. For example, if there are 20 people participating, everyone is potentially connected to 19 other people. 100% density (19/19) is the largest density in the system. The 5% density indicates there is only 1 of 19 possible connections.
- Centrality focuses on the behavior of each participant in a network. It measures the extent to which an individual interacts with other individuals in the network. The more individuals connected to others in the network, the greater their centrality in the network.
In-degree and out-degree variables are related to centrality.
- In degrees the centrality concentrates on the particular individual as the focal point; the centrality of all other individuals is based on their relationship to the individual focal point "in degrees".
- Degrees is a measure of centrality that still focuses on one individual, but its analytic relates to interactions that occur outside the individual; the measure of out-degree centrality is the number of times an individual's focal point interacts with others.
- Sosiogram is a visualization with the connection limit specified in the network. For example, a sociogram showing degrees of centrality for Participant A will illustrate all outgoing connections of Participant A made in the network under study.
Unique capabilities
Researchers use social network analysis in computer-supported collaborative learning studies in part because of the unique capabilities it offers. This particular method allows the study of patterns of interaction within the network learning community and can help illustrate the extent to which participants interact with other members of the group. Graphics created using the SNA tool provide visualization of the connections between participants and the strategies used to communicate within the group. Some authors also point out that SNA provides a method for analyzing the participant's pattern of participant changes easily from time to time.
A number of studies have applied SNA to CSCL in various contexts. Findings include a correlation between network density and teacher attendance, greater for "center" participants' recommendations, the frequency of cross-gender interaction within the network, and the relatively small role played by instructors in asynchronous learning networks..
Other methods used with SNA
Although many studies have shown the value of social network analysis in the field of computer-backed collaborative learning, researchers have suggested that SNA alone is not sufficient to achieve a full understanding of CSCL. The complexity of the interaction process and various data sources makes it difficult for SNA to provide in-depth analysis of CSCL. Researchers point out that SNA needs to be complemented by other analytical methods to form a more accurate picture of a collaborative learning experience.
A number of studies have combined other types of analysis with SNA in the CSCL study. This may be referred to as a multi-method approach or data triangulation, which will lead to increased reliability of evaluation in CSCL studies.
- Qualitative methods - Principles of qualitative case study research is a solid framework for integration of SNA methods in CSCL experience studies.
- Ethnographic data such as questionnaires and student interviews and non-participant class observations
- Case studies : learn comprehensively specific CSCL situations and associate findings with common schemes
- Content analysis: offers information about communication content among members
- Ethnographic data such as questionnaires and student interviews and non-participant class observations
- Quantitative methods - These include simple descriptive statistical analysis of events to identify the group members' specific attitudes that have not been traceable through SNA to detect general trends.
- Computer log files: provide automated data about how collaborative tools are used by learners
- Multidimensional scaling (MDS) : mapping the similarities between actors, so that more closely similar input data are closer to each other
- Software tools: QUEST, SAMSA (System for Adjacency Matrix and Sociogram-Based Analysis), and Nud * IST
See also
References
External links
Further reading
- Extraordinary Network Analysis (200 links to books, conferences, courses, journals, research groups, software, tutorials and more)
- Introduction to Stochastic-Based Actor Models for Network Dynamics - Snijders et al.
- Social and Organizational System Computing Analysis Center (CASOS) at Carnegie Mellon
- NetLab at the University of Toronto, studying the intersection of social networks, communications, information, and computing
- Netwiki (a wiki page devoted to social networking, maintained at the University of North Carolina at Chapel Hill)
- Program in Network Governance - Program on Network Governance, Harvard University
- International Workshop on Social Networking and Mining Analysis (SNA-KDD) - Annual workshop on social network analysis and mining, with participants from computer science, social sciences, and related disciplines.
- Historical Dynamics in Crisis: End of Byzantium, 1204-1453 (discussion of social network analysis from the point of view of historical study)
- Social Network Analysis: Systematic Approach to Investigation
Organization
- The International Network for Social Network Analysis
- Professional Platform for Organizational Network Analysis
Peer-reviewed journal
- Social Network
- Network Science
- Social Structure Journal
- Complex Network Journal
- Journal of Mathematical Sociology
- Social Networking and Mining Analysis (SNAM)
- "REDES". Spain: Universidad AutÃÆ'³noma de Barcelona y Universidad de Sevilla.
- "Connection". International Network for Social Network Analysis. Archived from the original in 2013-07-18.
Textbooks and educational resources
- Network, Crowd and Market (2010) by D. Easley & amp; J. Kleinberg
- Introduction to Social Networking Methods (2005) by R. Hanneman & amp; M. Riddle
- Social Network Analysis with Applications (2013) by I. McCulloh, H. Armstrong & amp; A. Johnson
- Social Network Analysis in Telecommunications (2011) by Carlos Andre Reis Pinheiro
Source of the article : Wikipedia
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