Data Preprocessing for Dynamic Social Network Analysis

Author(s):  
Preeti Gupta ◽  
Vishal Bhatnagar

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.

E-Marketing ◽  
2012 ◽  
pp. 185-197
Author(s):  
Przemyslaw Kazienko ◽  
Piotr Doskocz ◽  
Tomasz Kajdanowicz

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.


Author(s):  
Przemyslaw Kazienko ◽  
Piotr Doskocz ◽  
Tomasz Kajdanowicz

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.


Author(s):  
Sophie Mützel ◽  
Ronald Breiger

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.


2014 ◽  
Vol 926-930 ◽  
pp. 1680-1683
Author(s):  
Ying Ming Xu ◽  
Shu Juan Jin

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.


2017 ◽  
Vol 8 (4) ◽  
pp. 442-453 ◽  
Author(s):  
Allan Clifton ◽  
Gregory D. Webster

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.


2019 ◽  
Author(s):  
Xuanyi Li ◽  
Elizabeth A. Sigworth ◽  
Adrianne H. Wu ◽  
Jess Behrens ◽  
Shervin A. Etemad ◽  
...  

AbstractBackgroundClinical trials establish the standard of care for cancer and other diseases. While social network analysis has been applied to basic sciences, the social component of clinical trial research is not well characterized. We examined the social network of cancer clinical trialists and its dynamic development over more than 70 years, including the roles of subspecialization and gender in relation to traditional and network-based metrics of productivity.MethodsWe conducted a social network analysis of authors publishing chemotherapy-based prospective trials from 1946-2018, based on the curated knowledge base HemOnc.org, examining: 1) network density; 2) modularity; 3) assortativity; 4) betweenness centrality; 5) PageRank; and 6) the proportion of co-authors sharing the same primary cancer subspecialty designation. Individual author impact and productive period were analyzed as a function of gender and subspecialty.FindingsFrom 1946-2018, the network grew to 29,197 authors and 697,084 co-authors. While 99.4% of authors were directly or indirectly connected as of 2018, the network had very few connections and was very siloed by cancer subspecialty. Small numbers of individuals were highly connected and had disproportionate impact (scale-free effects). Women were under-represented and likelier to have lower impact, shorter productive periods (P<0.001 for both comparisons), less centrality, and a greater proportion of co-authors in their same subspecialty. The past 30 years were characterized by a trend towards increased authorship by women, with new author parity anticipated in 2032. However, women remain a distinct minority of first/last authors, with parity not anticipated for 50+ years.InterpretationThe network of cancer clinical trialists is best characterized as a strategic or “mixed-motive” network, with cooperative and competitive elements influencing its appearance.Network effects e.g., low centrality, which may limit access to high-profile individuals, likely contribute to ongoing disparities.FundingVanderbilt Initiative for Interdisciplinary Research; National Institutes of Health; National Science FoundationResearch in contextEvidence before this studyWe reviewed the literature on social networks from the 1800’s to 2018. Additionally, MEDLINE was searched for (“Social Networking”[Mesh] OR “Social Network Analysis”) AND (“Clinical Trials as Topic”[Mesh] OR “Hematology”[Mesh] OR “Medical Oncology”[Mesh]) without date restriction. The MEDLINE search yielded 43 results, of which 8 were relevant; none considered gender nor temporality in their analyses. To our knowledge, there has not been any similar study of the dynamic social network of clinical trialists from the inception of the fields of medical oncology and hematology to the present.Added value of this studyThis is the first dynamic social network analysis of cancer clinical trialists. We found that the network was sparse and siloed with a small number of authors having disproportionate impact and influence as measured by network metrics such as PageRank; these metrics have become more disproportionate over time. Women were under-represented and likelier to have lower impact, shorter productive periods, less network centrality, and a greater proportion of co-authors in their same cancer subspecialty.Implications of all the available evidenceWhile gender disparities have been demonstrated in many fields including hematology/oncology, our analysis is the first to show that network factors themselves are significantly implicated in gender disparity. The increasing coalescence of the network by traditional cancer type and around a small number of high-impact individuals implies challenges when the field pivots from traditionally disease-oriented subspecialties to a precision oncology paradigm. New mechanisms are needed to ensure diversity of clinical trialists.


2018 ◽  
Vol 4 (3) ◽  
pp. 204-218
Author(s):  
Mary B. Dunn

This article presents an experiential exercise where students learn the basics of social network analysis, relate social networks to social capital, and analyze their own networks in the classroom. Instructors of all types of courses at both the undergraduate and graduate levels can use this activity to teach students about social networks and build a greater sense of community in the classroom. This article provides instructions for collecting students’ social network data, teaching students about social networks as the basis for social capital, guiding students through basic social network analyses, and facilitating a discussion about ways to increase social capital for individuals and collectives. While engaging in this activity, students have opportunities to interact with other students and build high-quality relationships. In doing so, this exercise can facilitate a greater sense of community in the classroom, enrich the social capital for the collective, and promote students’ learning.


Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 2-6 ◽  
Author(s):  
Marijtje A. J. van Duijn ◽  
Jeroen K. Vermunt

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.


Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.


Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others.


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