| Location | Leacock Building, room 834 |
| Time | Winter 2024, Tuesdays and Thursdays 2:35–3:55pm (14h35-15h55) |
| Instructor |
Peter McMahan (peter.mcmahan@mcgill.ca; (514)398-6839) |
| Office hours | Wednesdays 11:00am-12:00pm or by appointment |
| Syllabus | https://soci424.netlify.com/ |
Social network analysis (SNA) encompasses an increasingly central set of methods and theories in the social sciences. This course will provide an overview of the foundational theories and core measures and methods used in the social-scientific literature. While students will learn to implement their own network analysis using quantitative methods, just as much emphasis will be placed on generating theoretically motivated analysis.
The class time is structured as a hybrid of lectures, seminars, and labs. Most of the classes will begin with a short presentation by the instructor, but much of the class time will be spent working alongside and in discussion with your classmates. The success of the course therefore relies on students’ careful readings of the assigned texts and critical engagement with the topics we will cover.
Two cross-listed courses will be held in parallel: SOCI 424 (undergraduate ) and SOCI 624 (graduate ). The content of the courses will be largely the same, with the primary difference being the final project. Students registered in SOCI 624 will be expected to complete a final research paper, while those registered in SOCI 424 will not (details below).
Students are expected to (1) closely read the assigned texts, (2) participate in discussions, (3) complete weekly worksheets and peer reviews, and (4) propose and complete a final project. Each of these expectations is detailed below.
The assigned readings are the core of the course material, and students are expected to carefully and critically read each assignment before class. To facilitate students’ engagement with the reading and to help prevent students from falling behind, we will use the online tool Perusall for all required readings. Perusall is a reading platform in which students annotate texts collaboratively alongside one another. More information on how Perusall works and how it is integrated into the course is available here. Instructions for accessing Perusall will be posted on the MyCourses site.
Readings will be graded as either complete (1 point) or incomplete (0 points). Student responses must demonstrate a thoughtful and thorough reading of the entire assignment to receive credit. At the end of the semester, the three lowest reading grades will be dropped from the assessment.
Our Thursday classes will be hybrid lecture/seminars, beginning with a brief participatory lecture covering major themes and methods from the required readings. We will then have a seminar-style discussion on the reading and the major concepts.
Our Tuesday classes will be labs, used to work on worksheets and individual projects in a collaborative environment (see following section).
There will be nine required worksheets over the course of the semester. Each worksheet will contain a mix of quantitative tasks and qualitative reflections. This will be an opportunity for students to practice the computational methods we learn and to discuss the empirical and theoretical implications of the computational results.
Each worksheet will be structured with a provided R Markdown document. R Markdown provides a way to mix code (in the R statistical language) and prose into a single document. Worksheets will be distributed ahead of Tuesday labs, and will be due Friday of the week they were distributed. While we will start the worksheets together in lab, each student will be responsible for completing their own worksheet by the due date.
Worksheets will be evaluated using peer assessment. After the deadline for a worksheet, each student will be responsible for evaluating an anonymized version of one of their classmate’s worksheets. The peer assessment is intended to expose students to different programming styles and interpretations of the data, and to encourage the production of readable R code.
Each student will finish an independent research project by the end of the semester.
The projects will be evaluated on the basis of three ( SOCI 424) or four ( SOCI 624) required assessments:
The content of the final project will differ for undergraduate and graduate students.
Undergraduate students ( SOCI 424) are expected to complete an independent analysis of a network or relational data set. Early in the term, I will share resources for finding such datasets. The analysis should ask a relevant question and provide a quantitative investigation into that question. The results of the analysis will be presented on the last day of class, but no paper will be required.
Graduate students ( SOCI 624) are expected to complete a fully developed research paper at the end of the term. This should consist of a theoretically motivated research question, appropriate data, and a thorough and robust analysis. Students will present a truncated version of their analysis on the final day of class, with a complete paper due after the end of classes.
Graduate students should arrange a brief meeting with me early in the term to discuss ideas for their research project and the appropriateness for the course.
| Percent of final grade | |||
|---|---|---|---|
| Component | Due | SOCI 424 | SOCI 624 |
| Reading | See schedule | 10% | 10% |
| Worksheets | See schedule | 35% | 25% |
| Worksheet peer evaluation | See schedule | 5% | 5% |
| Project précis | Thu, Feb 8 | 5% | 5% |
| Project proposal | Thu, Feb 29 | 20% | 10% |
| Project presentation | Wed, Apr 3 | 25% | 20% |
| Project paper | Fri, Apr 19 | N/A | 25% |
Students who need accommodation or who are having trouble accessing any aspect of the course may contact me directly. I will make every effort to accommodate individual situations, including religious, medical, or other personal circumstances.
Students with disabilities or otherwise in need of formal accommodation are encouraged to contact the Office for Student Accessibility & Achievement (formerly Office for Students with Disabilities: https://www.mcgill.ca/access-achieve/, phone 514-398-6009).
Les étudiants qui ont besoin d’un accommodation ou qui ont des difficultés à accéder à un aspect du cours peuvent me contacter directement. Je ferai tout mon possible pour tenir compte des circonstances individuelles, y compris des circonstances religieuses, médicales ou autres.
Les étudiants handicapés ou ayant besoin d’un aménagement formel sont encouragés à contacter le Service étudiant d’accessibilité et d’aide à la réussite (https://www.mcgill.ca/access-achieve/fr, téléphone 514-398-6009).
McGill University values academic integrity. Therefore, all students must understand the meaning and consequences of cheating, plagiarism and other academic offences under the Code of Student Conduct and Disciplinary Procedures (see http://www.mcgill.ca/students/srr/honest/ for more information).(approved by Senate on 29 January 2003)
L’université McGill attache une haute importance à l’honnêteté académique. Il incombe par conséquent à tous les étudiants de comprendre ce que l’on entend par tricherie, plagiat et autres infractions académiques, ainsi que les conséquences que peuvent avoir de telles actions, selon le Code de conduite de l’étudiant et des procédures disciplinaires (pour de plus amples renseignements, veuillez consulter le site http://www.mcgill.ca/students/srr/honest/).
In accord with McGill University’s Charter of Students’ Rights, students in this course have the right to submit in English or in French any written work that is to be graded. (approved by Senate on 21 January 2009)
Conformément à la Charte des droits de l’étudiant de l’Université McGill, chaque étudiant a le droit de soumettre en français ou en anglais tout travail écrit devant être noté (sauf dans le cas des cours dont l’un des objets est la maîtrise d’une langue).
Instructors and teaching assistants take the marking of assignments very seriously, and we work diligently to be fair, consistent, and accurate. Nonetheless, mistakes and oversights occasionally happen. If you believe that to be the case, you must adhere to the following rules:
Introduction
Sociologists talk about social structure, but what is that? We begin the course by defining the concept of social structure with an eye toward relational analysis and examining how it helps us ask and answer important questions. We will also introduce the primary formalization of social structure used in this class: the network.
Rawlings et al. (2023), Introduction and What is Social Structure? (not due on Perusall until following class)
Martin (2009), Social Structures Chapter 1
Network basics: dyadic data and network ties
Using networks to understand social phenomena raises a number of distinct questions for the kind of data we collect and use. We will formalize the representation of networks, discussing data formats, measurement, and network construction.
Rawlings et al. (2023), What Is a Social Network?
Borgatti and Halgin (2011) On Network Theory
Rawlings et al. (2023), How Are Social Network Data Collected?
Scott and Carrington (2011) A Brief Introduction to Analyzing Social Network Data
Starting small: relational building blocks of social structures
Dyads and triads are the foundation of network structures. This week, we will look at those structures in detail, describe the patterns they can follow, and look at the implications of those patterns for the individuals in a network and for networks as a whole.
Rawlings et al. (2023), Structuration and Egocentric Networks and Sociality and Elementary Forms of Structure
McPherson, Smith-Lovin, and Cook (2001) Birds of a Feather: Homophily in Social Networks
Structural components: grouping vertices within networks
Many social structures can be meaningfully partitioned into different groups of members, and even those that cannot often have distinctive groups or clusters embedded within them. We will examine theories and common methods of identifying cliques, groups, clusters, and subcommunities of vertices within networks.
Rawlings et al. (2023), Cohesion and Groups
Shwed and Bearman (2010) The Temporal Structure of Scientific Consensus Formation
The individual in the crowd: what networks can tell us about their members
The ability to talk about individuals’ position within a larger structure has become one of the most useful tools to come out of network analysis. We will look at different notions of network centrality, hierarchy, and other forms of privileged positions.
Rawlings et al. (2023), Hierarchy and Centrality
Faris and Felmlee (2011) Status Struggles: Network Centrality and Gender Segregation in Same- and Cross-Gender Aggression
Discovering roles: structural equivalence
Structural equivalence categorizes nodes in a network by their patterns of relation to the other nodes in that network. We will discuss how structural equivalence classes are similar to and distinct from the clusters and cliques we have looked at, and we will consider the idea of ‘roles’ within a network structure. This will also be our first look at statistical inference on networks.
Rawlings et al. (2023), Positions and Roles
Padgett and Ansell (1993) Robust Action and the Rise of the Medici, 1400-1434
Institutions influence structure: affiliation networks
The edges in a network can represent a wide array of different relations between its members, but affiliation edges—those defined by co-membership in some category—yield unique structural outcomes. This week will focus on the particular features of affiliation networks and the insights they have provided into institutional structure.
Rawlings et al. (2023), Affiliations and Dualities
Valeeva, Heemskerk, and Takes (2020) The duality of firms and directors in board interlock networks: A relational event modeling approach
Breiger (1974) The Duality of Persons and Groups
Relations without networks?: relational sociology and field theory
Although the network formalization has come to dominate the quantitative literature on social structures, there is a great deal of sociological work on relations and relational structures that is not based on networks. We will discuss the dominant approaches in relational sociology
Rawlings et al. (2023), Networks and Culture
Mohr (2013) Bourdieu’s Relational Method in Theory and in Practice: From Fields and Capitals to Networks and Institutions (and Back Again)
D’Esposito, De Stefano, and Ragozini (2014) On the use of Multiple Correspondence Analysis to visually explore affiliation networks
Mapping class and culture: structure from language and behavior
Most of the social structures we have looked at so far concern relations between people. This week we will consider networks that use language, tastes, and behaviors as vertices. Networks like this can be used to map the social structures of class, taste, and other cultural objects.
Hoffman et al. (2018) The (Protestant) Bible, the (printed) sermon, and the word(s): The semantic structure of the Conformist and Dissenting Bible, 1660–1780
Lee and Martin (2015) Coding, counting and cultural cartography
Kozlowski, Taddy, and Evans (2019) The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings
Explaining network ties (regression and ERGM)
“The primary aim of quantitative sociological research is explanation. Beyond simply describing patterns, statistical models and social theory allow us to understand what drives those patterns. Statistical modeling of network ties poses an array of problems for the kinds of statistical analyses typically used in sociology. This week will give a brief overview of the difficulties of tie prediction and the most prominent solution: exponential random graph models (ERGMs)”
Rawlings et al. (2023), Models for Networks
Transmission through networks: social influence and contagion
Networks are an especially useful tool for understanding the transmission of ideas, knowledge, disease, and behavior among groups. We will consider different models of network diffusion and discuss the role of structure in diffusion processes.
Rawlings et al. (2023), Models for Network Diffusion
Aral, Muchnik, and Sundararajan (2009) Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks
Bettinger, Liu, and Loeb (2016) Connections Matter: How Interactive Peers Affect Students in Online College Courses
Centola (2010) The Spread of Behavior in an Online Social Network Experiment
Models of network dynamics
The evolution of networks is a complex topic. Node-level attributes and relation-level edges influence one another in ways that are difficult to trace. This week will consist of a brief overview of statistical models of network dynamics, with a focus on stochastic actor oriented models (SAOMs).
Rawlings et al. (2023), Models for Social Influence
Block, Stadtfeld, and Snijders (2019) Forms of Dependence: Comparing SAOMs and ERGMs From Basic Principles
Student presentations