Models And Methods In Social Network Analysis Pdf

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Social Network Analysis: Methods and Applications

Social network analysis SNA is the process of investigating social structures through the use of networks and graph theory. Examples of social structures commonly visualized through social network analysis include social media networks , [2] [3] memes spread, [4] information circulation, [5] friendship and acquaintance networks , business networks, knowledge networks, [6] [7] difficult working relationships, [8] social networks, collaboration graphs , kinship , disease transmission , and sexual relationships.

These visualizations provide a means of qualitatively assessing networks by varying the visual representation of their nodes and edges to reflect attributes of interest. Social network analysis has emerged as a key technique in modern sociology. It has also gained significant popularity in the following - anthropology , biology , [12] demography , communication studies , [3] [13] economics , geography , history , information science , organizational studies , [6] [8] political science , public health, [14] [7] social psychology , development studies , sociolinguistics , and computer science [15] and is now commonly available as a consumer tool see the list of SNA software.

Social scientists have used the concept of " social networks " since early in the 20th century to connote complex sets of relationships between members of social systems at all scales, from interpersonal to international.

In the s Jacob Moreno and Helen Jennings introduced basic analytical methods. White , and Harrison White expanded the use of systematic social network analysis.

Homophily : The extent to which actors form ties with similar versus dissimilar others. Similarity can be defined by gender, race, age, occupation, educational achievement, status, values or any other salient characteristic. Multiplexity: The number of content-forms contained in a tie. Network Closure : A measure of the completeness of relational triads.

An individual's assumption of network closure i. Transitivity is an outcome of the individual or situational trait of Need for Cognitive Closure. Propinquity : The tendency for actors to have more ties with geographically close others.

Bridge : An individual whose weak ties fill a structural hole , providing the only link between two individuals or clusters. It also includes the shortest route when a longer one is unfeasible due to a high risk of message distortion or delivery failure.

Centrality : Centrality refers to a group of metrics that aim to quantify the "importance" or "influence" in a variety of senses of a particular node or group within a network. Density : The proportion of direct ties in a network relative to the total number possible. 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'.

Structural holes: The absence of ties between two parts of a network. Finding and exploiting a structural hole can give an entrepreneur a competitive advantage. This concept was developed by sociologist Ronald Burt , and is sometimes referred to as an alternate conception of social capital. Tie Strength: Defined by the linear combination of time, emotional intensity, intimacy and reciprocity i.

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. Clustering coefficient : A measure of the likelihood that two associates of a node are associates.

A higher clustering coefficient indicates a greater 'cliquishness'. Cohesion: The degree to which actors are connected directly to each other by cohesive bonds. Structural cohesion refers to the minimum number of members who, if removed from a group, would disconnect the group.

Visual representation of social networks is important to understand the network data and convey the result of the analysis.

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. 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. 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 [50] [51] 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 [53] 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 an espionage ring, an organized crime family or a street gang. The National Security Agency NSA uses its 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.

In other approaches, textual analysis is carried out considering the network of words co-occurring in a text see for example the Semantic Brand Score. 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. 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.

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, [67] a greater regard for the recommendations of "central" participants, [69] infrequency of cross-gender interaction in a network, [70] 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, [67] 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 quantitative application to social media, see social media analytics. For social networking sites, see social networking service.

For other uses, see Social network disambiguation. Analysis of social structures using network and graph theory. Historical perspectives. Conflict theory Structural functionalism Positivism Social constructionism.

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 Attention inequality.

Journal of Information Science. Social Science Computer Review. Memoria e Ricerca 2 : — Research Policy. The Gap Between Discovery and Delivery". American Journal of Preventive Medicine. Academy of Management Journal. Social Network Analysis in Telecommunications. In Abraham, Ajith ed.

Social Network Analysis in Predictive Policing

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Social network analysis

Social network analysis SNA is the process of investigating social structures through the use of networks and graph theory. Examples of social structures commonly visualized through social network analysis include social media networks , [2] [3] memes spread, [4] information circulation, [5] friendship and acquaintance networks , business networks, knowledge networks, [6] [7] difficult working relationships, [8] social networks, collaboration graphs , kinship , disease transmission , and sexual relationships. These visualizations provide a means of qualitatively assessing networks by varying the visual representation of their nodes and edges to reflect attributes of interest. Social network analysis has emerged as a key technique in modern sociology. It has also gained significant popularity in the following - anthropology , biology , [12] demography , communication studies , [3] [13] economics , geography , history , information science , organizational studies , [6] [8] political science , public health, [14] [7] social psychology , development studies , sociolinguistics , and computer science [15] and is now commonly available as a consumer tool see the list of SNA software.

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Models and Methods in Social Network Analysis (Structural Analysis in the Social Sciences)

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Models and Methods in Social Network Analysis presents the most important developments in quantitative models and methods for analyzing social network data that have appeared during the s. View via Publisher. Save to Library. Create Alert.

Agent-based modelling ABM and social network analysis SNA are both valuable tools for exploring the impact of human interactions on a broad range of social and ecological patterns. Integrating these approaches offers unique opportunities to gain insights into human behaviour that neither the evaluation of social networks nor agent-based models alone can provide. There are many intriguing examples that demonstrate this potential, for instance in epidemiology, marketing or social dynamics. Based on an extensive literature review, we provide an overview on coupling ABM with SNA and evaluating the integrated approach. Building on this, we identify current shortcomings in the combination of the two methods.

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PDF | On Feb 7, , Peter J. Carrington and others published Models and Methods in Social Network Analysis | Find, read and cite all the.


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Models and methods in social network analysis

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