Network Analysis
A.Y. 2024/2025
Learning objectives
Learning to pursue an empirical study explaining structures of social relationships or analysing the effect
of social relationships on other social outcomes.
of social relationships on other social outcomes.
Expected learning outcomes
- assessing whether and which social network research design is appropriate to a target
research question
- designing a network survey
- computing statistics to describe network- and individual-level properties
- testing hypotheses of causal mechanisms through state-of-the-art statistical and computational modelling
research question
- designing a network survey
- computing statistics to describe network- and individual-level properties
- testing hypotheses of causal mechanisms through state-of-the-art statistical and computational modelling
Lesson period: Second trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
Second trimester
Course syllabus
The course will be composed of four main parts, mirroring the real process of an empirical social network research project: 1) epistemology and methodology of social network analysis, 2) research design and data collection, 3) describing and exploring network data through descriptive statistics, 4) hypothesis testing through statistical and computational modelling. More specifically:
- why and when engaging in social network research
- graph theory: basic concepts
- types of social relationships and relational data
- social networks as models of mechanisms and processes
- research design: sociocentric (full-network) vs. egocentric (ego-network)
- designing a survey: 'name generators' and 'edge interpreters'
- measuring social networks
- data structures: adjacency matrices and edgelists
- data management with 'igraph' and 'tidygraph'
- visualization with 'ggraph'
- connectivity: density, cohesion, degree distribution
- centrality: eigenvector, closeness, betweenness
- centralisation and clustering
- ego-networks: data management and analysis
- bipartite networks: data management, projection, analysis
- random graph models
- statistical models of social networks: inference and dependency
- Exponential Random Graph Models (ERGM)
- testing hypotheses through ERGMs with 'statnet'
- Stochastic Actor-Oriented Models (SAOM) for panel data
- Agent-Based Models (ABM) of behaviour-network co-evolution
- why and when engaging in social network research
- graph theory: basic concepts
- types of social relationships and relational data
- social networks as models of mechanisms and processes
- research design: sociocentric (full-network) vs. egocentric (ego-network)
- designing a survey: 'name generators' and 'edge interpreters'
- measuring social networks
- data structures: adjacency matrices and edgelists
- data management with 'igraph' and 'tidygraph'
- visualization with 'ggraph'
- connectivity: density, cohesion, degree distribution
- centrality: eigenvector, closeness, betweenness
- centralisation and clustering
- ego-networks: data management and analysis
- bipartite networks: data management, projection, analysis
- random graph models
- statistical models of social networks: inference and dependency
- Exponential Random Graph Models (ERGM)
- testing hypotheses through ERGMs with 'statnet'
- Stochastic Actor-Oriented Models (SAOM) for panel data
- Agent-Based Models (ABM) of behaviour-network co-evolution
Prerequisites for admission
This course requires basic understanding of probability theory, inferential statistics, and maximum likelihood estimation, as usually covered by any master-level statistics or multivariate analysis courses. Students are strongy recommended to attend this course only if they have working knowledge of the 'R' programming language, as covered by the introductory unit provided by the COM 'Data analysis' course.
Teaching methods
The course will be held through interactive lectures and practical coding sessions. Students will be required to actively engage in collective discussions and actively participate to practical sessions.
Teaching Resources
The main content of the course will be backed by:
- Bonacich, P., & Lu, P. (2012). Introduction to Mathematical Sociology. Princeton, NJ: Princeton University Press, Ch. 1-4, 7-8, 14
- Borgatti, S.P., Everett, M.G., & Johnson, J.C. (2013). Analyzing Social Networks. London: Sage. (selected chapters)
- Lusher, D., Koskinen, J., & Robins, G. (Eds.) (2013). Exponential Random Graph Models for Social Networks. Theory, Methods, and Applications. New York, NY: Cambridge University Press, Ch. 2-4.
- Rawlings, C.M., Smith, J.A., Moody, J., & McFarland, D.A. (2023). Network Analysis: Integrating Social Network Theory, Method, and Application with R. Cambridge: Cambridge University Press. (selected chapters)
- Robins, G. (2015). Doing Social Network Research. Network-Based Research Design for Social Scientists. London: Sage. (selected chapters)
Material for practical sesssions will be based on:
- Bianchi, F., Casnici, N., & Squazzoni, F. (2018). Solidarity as a byproduct of professional collaboration. Social support and trust in a coworking space. Social Networks, 54: 61-72. doi: 10.1016/j.socnet.2017.12.002
- Bianchi, F., Piolatto, M., Marengoni, A., & Squazzoni, F. (2023). Structure of personal networks and cognitive abilities: a study on a sample of Italian older adults. Social Networks, 74: 71-77. doi: 10.1016/j.socnet.2023.02.005
- Bonacich, P., & Lu, P. (2012). Introduction to Mathematical Sociology. Princeton, NJ: Princeton University Press, Ch. 1-4, 7-8, 14
- Borgatti, S.P., Everett, M.G., & Johnson, J.C. (2013). Analyzing Social Networks. London: Sage. (selected chapters)
- Lusher, D., Koskinen, J., & Robins, G. (Eds.) (2013). Exponential Random Graph Models for Social Networks. Theory, Methods, and Applications. New York, NY: Cambridge University Press, Ch. 2-4.
- Rawlings, C.M., Smith, J.A., Moody, J., & McFarland, D.A. (2023). Network Analysis: Integrating Social Network Theory, Method, and Application with R. Cambridge: Cambridge University Press. (selected chapters)
- Robins, G. (2015). Doing Social Network Research. Network-Based Research Design for Social Scientists. London: Sage. (selected chapters)
Material for practical sesssions will be based on:
- Bianchi, F., Casnici, N., & Squazzoni, F. (2018). Solidarity as a byproduct of professional collaboration. Social support and trust in a coworking space. Social Networks, 54: 61-72. doi: 10.1016/j.socnet.2017.12.002
- Bianchi, F., Piolatto, M., Marengoni, A., & Squazzoni, F. (2023). Structure of personal networks and cognitive abilities: a study on a sample of Italian older adults. Social Networks, 74: 71-77. doi: 10.1016/j.socnet.2023.02.005
Assessment methods and Criteria
Attending students:
- active participation to classes + assignments (25%)
- ~5,000-word paper (individual or team work, to be presented orally after previous submission) reporting one of the following: a) analysis of network data (either provided by the teacher or collected by self); b) critical analysis of a previously assigned article reporting empirical network research; c) design of an empirical network research project
- optional oral exam (+10%/-10% of paper grade)
Non-attending students:
~10,000-word paper (individual work) on one of the above options.
- active participation to classes + assignments (25%)
- ~5,000-word paper (individual or team work, to be presented orally after previous submission) reporting one of the following: a) analysis of network data (either provided by the teacher or collected by self); b) critical analysis of a previously assigned article reporting empirical network research; c) design of an empirical network research project
- optional oral exam (+10%/-10% of paper grade)
Non-attending students:
~10,000-word paper (individual work) on one of the above options.
SPS/07 - GENERAL SOCIOLOGY - University credits: 6
Lessons: 40 hours
Professor:
Bianchi Federico
Shifts:
Turno
Professor:
Bianchi FedericoProfessor(s)