An Introduction to Social Network Analysis for Personality and Social Psychologists

2017 ◽  
Vol 8 (4) ◽  
pp. 442-453 ◽  
Author(s):  
Allan Clifton ◽  
Gregory D. Webster
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Groups ◽  
Network Data ◽  
Personality Psychology ◽  
Social Network Data ◽  
And Behavior

Social network analysis (SNA) is a methodology for studying the connections and behavior of individuals within social groups. Despite its relevance to social and personality psychology, SNA has been underutilized in these fields. We first examine the paucity of SNA research in social and personality journals. Next we describe methodological decisions that must be made before collecting social network data, with benefits and drawbacks for each. We discuss common SNAs and give an overview of software available for SNA. We provide examples from the literature of SNA for both one-mode and two-mode network data. Finally, we make recommendations to researchers considering incorporating SNA into their research.

Social Network Analysis in Marketing

E-Marketing ◽  
2012 ◽  
pp. 185-197
Author(s):  
Przemyslaw Kazienko ◽  
Piotr Doskocz ◽  
Tomasz Kajdanowicz
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relationships ◽  
Network Data ◽  
Social Network Data ◽  
The Social ◽  
Targeted Marketing ◽  
Potential Customers ◽  
Demographic Features

The chapter describes a method how to perform a classification task without any demographic features and based only on the social network data. The concept of such collective classification facilitates to identify potential customers by means of services used or products purchased by the current customers, i.e. classes they belong to as well as using social relationships between the known and potential customers. As a result, a personalized offer can be prepared for the new clients. This innovative marketing method can boost targeted marketing campaigns.

Social Network Analysis in Marketing

2011 ◽  
pp. 271-283
Author(s):  
Przemyslaw Kazienko ◽  
Piotr Doskocz ◽  
Tomasz Kajdanowicz
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relationships ◽  
Network Data ◽  
Social Network Data ◽  
The Social ◽  
Targeted Marketing ◽  
Potential Customers ◽  
Demographic Features

The chapter describes a method how to perform a classification task without any demographic features and based only on the social network data. The concept of such collective classification facilitates to identify potential customers by means of services used or products purchased by the current customers, i.e. classes they belong to as well as using social relationships between the known and potential customers. As a result, a personalized offer can be prepared for the new clients. This innovative marketing method can boost targeted marketing campaigns.

Data Preprocessing for Dynamic Social Network Analysis

2013 ◽  
pp. 25-39 ◽  
Author(s):  
Preeti Gupta ◽  
Vishal Bhatnagar
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Data Preprocessing ◽  
Original Data ◽  
Network Data ◽  
Data Set ◽  
Social Network Data ◽  
Dynamic Social Network ◽  
The Social

The social network analysis is of significant interest in various application domains due to its inherent richness. Social network analysis like any other data analysis is limited by the quality and quantity of data and for which data preprocessing plays the key role. Before the discovery of useful information or pattern from the social network data set, the original data set must be converted to a suitable format. In this chapter we present various phases of social network data preprocessing. In this context, the authors discuss various challenges in each phase. The goal of this chapter is to illustrate the importance of data preprocessing for social network analysis.

scholarly journals Birds of a Feather Flock Together and Opposites Attract: The Nonlinear Relationship Between Personality and Friendship

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Haiyan Liu ◽  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relations ◽  
Research Question ◽  
Psychological Research ◽  
Personality Psychology ◽  
Analysis Techniques ◽  
Social Network Data ◽  
Social Entities

Whether birds of a feather flock together or opposites attract is a classical research question in social and personality psychology. In most existing studies, correlation-based techniques are commonly used to study the similarity/dissimilarity among social entities. Social network data comprises two primary components: actors and the possible social relations between them. It, therefore, has observations on both the dyads with and without social relations. Because of the availability of the baseline group (dyads without social relations), it is possible to contrast the two groups of dyads using social network analysis techniques. This study aims to illustrate how to use social network analysis techniques to address psychological research questions. Specifically, we will investigate how the similarity or dissimilarity of actor's characteristics relates to the likelihood for them to build social relations. By analyzing a college friendship network, we found the quadratic relations between personality similarity and friendship. Both very similar and very dissimilar personalities boost friendship among college students.

What Is Special About Social Network Analysis?

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 2-6 ◽  
Author(s):  
Marijtje A. J. van Duijn ◽  
Jeroen K. Vermunt
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Statistical Models ◽  
Network Modeling ◽  
Network Data ◽  
Short Introduction ◽  
Social Network Data ◽  
Social Network Modeling ◽  
Recent Developments

In a short introduction on social network analysis, the main characteristics of social network data as well as the main goals of social network analysis are described. An overview of statistical models for social network data is given, pointing at differences and similarities between the various model classes and introducing the most recent developments in social network modeling.

Duality beyondPersons and Groups

2020 ◽  
pp. 391-413
Author(s):  
Sophie Mützel ◽  
Ronald Breiger
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Data ◽  
Future Directions ◽  
Affiliation Networks ◽  
The Social ◽  
Recent Developments ◽  
Principle Of Duality ◽  
Seminal Article

This chapter focuses on the general principle of duality, which was originally introduced by Simmel as the intersection of social circles. In a seminal article, Breiger formalized Simmel’s idea, showing how two-mode types of network data can be transformed into one-mode networks. This formal translation proved to be fundamental for social network analysis, which no longer needed data on who interacted with whom but could work with other types of data. In turn, it also proved fundamental for the analysis of how the social is structured in general, as many relations are dual (e.g. persons and groups, authors and articles, organizations and practices), and are thus susceptible to an analysis according to duality principles. The chapter locates the concept of duality within past and present sociology. It also discusses the use of duality in the analysis of culture as well as in affiliation networks. It closes with recent developments and future directions.

scholarly journals Scientific collaboration network ammong Brazilian universities

2013 ◽  
Vol 6 (1) ◽  
Author(s):  
Maria Isabel Escalona-Fernandez ◽  
Antonio Pulgarin-Guerrero ◽  
Ely Francina Tannuri de Oliveira ◽  
Maria Cláudia Cabrini Gracio
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Scientific Collaboration ◽  
Methodological Approach ◽  
Scientific Production ◽  
Collaboration Network ◽  
Network Data ◽  
New Knowledge

This paper analyses the scientific collaboration network formed by the Brazilian universities that investigate in dentistry area. The constructed network is based on the published documents in the Scopus (Elsevier) database covering a period of 10 (ten) years. It is used social network analysis as the best methodological approach to visualize the capacity for collaboration, dissemination and transmission of new knowledge among universities. Cohesion and density of the collaboration network is analyzed, as well as the centrality of the universities as key-actors and the occurrence of subgroups within the network. Data were analyzed using the software UCINET and NetDraw. The number of documents published by each university was used as an indicator of its scientific production.

Using Social Network Analysis (SNA) to Study Members and Activists of Political Parties

2020 ◽  
Vol 147-148 (1-2) ◽  
pp. 13-48
Author(s):  
Emilien Paulis
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Political Parties ◽  
Political Party ◽  
Methodological Approach ◽  
Network Data ◽  
Party Member ◽  
Party Membership ◽  
Local Party

This article explores the development of my PhD dissertation’s methodological approach, based on Social Network Analysis (SNA), or the collection and analysis of network data, in order to deal with political parties and their members (party membership). I extensively relied on this alternative, growing methodological background in three extents. First (1), SNA was used to analyze bibliographic references related to my dissertation topic, i.e. party membership studies, and identify the most central authors, thereby illustrating the literature review while describing their key contributions. Second (2), SNA was employed to collect and analyze network data likely to better grasp how interpersonal networks affect the probability for a random citizen to turn into party member, assuming that social influence matters in the process of joining a political party. Third (3), I further capitalized on SNA to deal with the question of party activism and why some members become active whereas others remain passive, arguing theoretically and showing empirically that part of the answer lies in members’ position within their local party branch’s social network. Each of these three applications is discussed in the light of the main methodological developments, the empirical findings and their interpretation, while shortcomings and research opportunities are more systematically highlighted at the end.

Application of Computer System in the Study of Sociology

2014 ◽  
Vol 926-930 ◽  
pp. 1680-1683
Author(s):  
Ying Ming Xu ◽  
Shu Juan Jin
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Computer Algorithms ◽  
Useful Knowledge ◽  
Social Network Data ◽  
Analysis Task ◽  
Original Analysis ◽  
The Social ◽  
The Relationship

With the development of information technology, more and more data about social to be collected. If we can analyze them effectively, it will help people to understand sociological understanding, promoting the development of social science. But the increasing amount of data and analysis to put forward a huge challenge. Now the social networks have already surpassed the processing ability of the original analysis means, must use a more effective tool to complete the analysis task. The computer as a way of helping people from massive data to find the potential useful knowledge tools, play an important role in many fields. Social network analysis, also known as link mining, refers to the handling of the relationship between social network data in the computer method. In this paper, the methods of computer and the social network analysis was introduced in this paper and the computer algorithms are summarized in the application of social network analysis.

scholarly journals Common Permutation Methods in Animal Social Network Analysis Do Not Control for Non-independence

2021 ◽  
Author(s):  
Jordan D. A. Hart ◽  
Michael N. Weiss ◽  
Lauren J. N. Brent ◽  
Daniel W. Franks
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Regression Models ◽  
Permutation Tests ◽  
Network Data ◽  
P Values ◽  
Parametric Regression ◽  
Node Label ◽  
Parametric Regression Models

The non-independence of social network data is a cause for concern among behavioural ecologists conducting social network analysis. This has led to the adoption of several permutation-based methods for testing common hypotheses. One of the most common types of analysis is nodal regression, where the relationships between node-level network metrics and nodal covariates are analysed using a permutation technique known as node-label permutation. We show that, contrary to accepted wisdom, node-label permutations do not account for the types of non-independence assumed to exist in network data, because regression-based permutation tests still assume exchangeability of residuals. The same theoretical condition also applies to the quadratic assignment procedure (QAP), a permutation-based method often used for conducting dyadic regression. We highlight that node-label permutations produce the same p-values as equivalent parametric regression models, but that in the presence of confounds, parametric regression models produce more accurate effect size estimates. We also note that QAP only controls for a specific type of non-independence between edges that are connected to the same nodes, and that appropriate parametric regression models are also able to account for this type of non-independence. Based on this, we advocate the retirement of permutation tests for regression analyses, in favour of well-specified parametric models. Moving away from permutation-based methods will reduce over-reliance on p-values, generate more reliable estimates of effect sizes, and facilitate the adoption of more powerful types of statistical analysis.

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