2024/2025
Анализ социальных сетей в R
Статус:
Маго-лего
Когда читается:
1, 2 модуль
Онлайн-часы:
40
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
6
Контактные часы:
12
Course Syllabus
Abstract
This course contains three independent, but interconnected components: 1. Theoretical: network theory and theory of networks, and their role in homological network of focal constructs of interest; 2. Methodological: methods of analysis and software programs used to analyze network data; 3. Applied: the theory and instruments learned in class are then used in individual and group work to design a research project in student’s own area of interest.
Learning Objectives
- To develop and/or foster critical reviewing skills of published empirical research using network analytic methods.
- To provide students with an understanding of the basic principles of network analysis and lay the foundation for future learning in the area.
- To explore the advantages and disadvantages of various network analytic tools and methods, and demonstrate how they relate to other methods of analysis.
- To develop student familiarity, through hands-on experience, with the major network modeling programs, so that they can use them and interpret their output.
- To develop student familiarity, through hands-on experience, with the major network modeling programs, so that they can use them and interpret their output.
Expected Learning Outcomes
- Students will gain knowledge of graphs (Bipartite graphs, Trees, Spanning tree, Directed graphs, De Bruijn graphs).
- Define key concepts of critical discourse analysis and explain their practical application
- Describe methods of critical discourse studies and linguistic features whose analysis these methods involve
- Provide a brief and systematic understanding of the history of the birth of the statistical environment and programming language, the fundamental differences between the R language and its analogues.
- Formulate the conceptual basis (structure) of the R language and the principles of the environment, acquire skills in organizing the work of a session with this environment.
- Introduce a classifiction of basic types of data storage and processing, the basics of functional programming.
- Learn the basic programming models in R, including how to manage data, code, and procedures, including through additional packages, and special data types (factors).
- Learn how to independently load and export any type of data, using the maximum capabilities of modern data development and analysis tools (using software "assistants").
- Gain a systematic view of graph theory, understanding of theories and current research in sociology related to social network analysis.
- Application of the basic properties and principles towards recording graphs in a mathematical model, including R code implementation.
- Master working with different formats of storing network data in the R statistical environment, including conducting basic analytics, visualization and exporting the obtained results.
- Master basic practices of working with the igraph package, including data loading, primary analysis, visualization, exporting the obtained results to various files.
- Provide a practical implication of the methodology of advanced network data analysis with an emphasis on network structure segmentation.
- Become familiar with further methods of analyzing the internal network structure, with an emphasis on the status/role positions of some research objects
- Systematize the basic requirements and features of preparing network data for regressions and advanced network modeling
- Apply network analysis methodology to study changes in network structures over time, including classification and structural aspects.
Course Contents
- [Week 1] R concepts – Part 1: Introduction
- [Week 2] R concepts – Part 2: Essential practices
- [Week 3] Introduction to social network concepts (Part 1)
- [Week 4] Introduction to social network concepts (Part 2)
- [Week 5] Network Analysis: Working tools overview
- [Week 6] Data loading and processing: EDA perspective
- [Week 7] Community detection: Social Network Segmentation
- [Week 8] Bipartite Projection: Study complex networks
- [Week 9] Advances in network exploration: ego networks, cliques and information flow
- [Week 10] Network predictions using regression framework (intro to ERGM)
- [Week 11] Social Network Analysis and Causation: Dynamic approach
- [Week 12] The Semantic Network Approach: Study of the Network of Socially Driven Discourse
Assessment Elements
- Quizes and Tests (After Videos)This form of control implies a series of closed and semi-open questions (substitute a word / number), compiled on the basis of video lecture materials. This form of control is not evaluated, however, it allows the student to successfully pass the final test within the framework of the thematic week. Important! The number or re-take attempts is unlimited for this form of control.
- Quizes and Practical Tasks (Week Revision)This form of control is the performance of test and practical tasks dedicated to the content of the thematic week. The student is offered a set of test tasks with a choice of one answer, several answers, or semi-open questions (enter a word, write a small code). The task of these test tasks is to help the student systematize the accumulated understanding of the content of the thematic week and prepare for the study of the next topic. Important! The number or re-take attempts is *three* for this form of control.
- Laboratory Assignments (Extended Practice)The laboratory work is an extended holistic task, similar to the case study format. As part of this assignment, students must complete a series of tasks that will allow them to carry out a full cycle of analysis of social network data and formulate meaningful results.
- Final ProjectThe goal of the project is to offer an example of a complete cycle using real and / or training (test) data functions, with the active implementation of this process, both theoretical description and empirical processing. Overall, the final product of the work is a series of presentation material (flash cards) that contain a full range of different types, properties of "typical" studies alongside with a Jupyter Notebook contaning all the code and interpretation material. This for of assignment is done individually (in special cases -- max by 2 students per project).
Interim Assessment
- 2024/2025 2nd module0.3 * Final Project + 0.4 * Laboratory Assignments (Extended Practice) + 0.3 * Quizes and Practical Tasks (Week Revision)
Bibliography
Recommended Core Bibliography
- Crawley, M. J. (2013). The R Book (Vol. Second Edition). Chichester, West Sussex: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=531630
- Diestel R. Graph Theory. – Springer, 2017. – 428 pp.
- Eric D. Kolaczyk, & Gábor Csárdi. (2020). Statistical Analysis of Network Data with R: Vol. 2nd ed. Springer.
- Luke, D. A. (2015). A User’s Guide to Network Analysis in R. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1114415
- Wasserman, S., & Faust, K. (1994). Social Network Analysis : Methods and Applications. Cambridge: Cambridge eText. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=490515
Recommended Additional Bibliography
- Fu, X., Luo, J.-D., & Boos, M. (2017). Social Network Analysis : Interdisciplinary Approaches and Case Studies. Boca Raton, FL: CRC Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1499393
- Omar Trejo, & Peter C. Figliozzi. (2017). R Programming By Example : Practical, Hands-on Projects to Help You Get Started with R. Packt Publishing.