The course is directed to anyone involved and/or interested in network analysis, especially professionals with a social science background, and students of statistics, economics, political science, and sociology.

Prerequisites for the course are basic knowledge of statistics. Previous use of R and R-Studio is required. 

Students may work through one or more R tutorials prior to the first meeting. Some resources can be found here: https://rstudio.cloud/learn/primers.

The goal of the course is to give an introduction to Social Network Analysis and relational data, with an explicit practical intent.

The course has a hands-on character, and the class will apply the concepts presented by the instructor using R packages (igraph in particular) and RStudio: R will be used most of the time, with some incursion into Gephi, for large networks visualisations.

Knowledge of R is therefore recommended to get the best out of this applied introduction to Social Network Analysis. 

The course will cover the following topics (just sketched topics in italics):

General introduction to Network Analysis

• History of (Social) Network Analysis
• Terminology and basic definitions
• Software
• Data

Types of Networks

• Directed and undirected graphs
• Weighted and unweighted graphs
• Two mode networks
• Multiplex

Network Data

• Imputing network data
• Manipulating network objects: Nodes, Edges and Graph Attributes
• Generating networks: Random Graphs, Small-World models, Preferential Attachment

Visualising Networks

• Introduction to layout algorithms
• Elements of networks visualisations
• Decorating Graphs Layouts
• Visualising Large Networks

Centrality

• Degree centrality
• Closeness centrality
• Betweenness centrality
• Eigenvector centrality

Network Cohesion

• Subgraphs and Censuses
• Density and relative frequencies of edges
• Connectivity and cuts
• Assortativity and segregation

Graph Partitioning

• Hierarchical clustering
• Spectral partitioning
• Modularity and communities in networks

Statistical Models for Network Data

• Correlation networks
• Propensity score with network data

Network Analysis is a rapidly growing field among quantitative social sciences. Networks permeate everyday life and contemporary social phenomena are increasingly represented as networks: from the participation to online social media, to the structure of relations in a working environment, to the description of political movements, to the analysis of the diffusion of banking crises. The boundaries of Network Science go well beyond social sciences, including applications to history, biology, glottology, statistics, and computer science.

Network Analysis focuses on relations, and studies those relations through the tools of Graph Theory. This course will introduce the participants to Applied Network Analysis concentrating on definition, measurement, data collection and manipulation, description and visualisation of networks. Some inferential statistical techniques will be presented at the end of the course. Examples will be drawn from economics, political science, and sociology, and replication exercises will be discussed in class and implemented by the participants. 

The course has a hands-on character, and the class will apply the concepts presented by the instructor using R packages (igraph in particular) and RStudio: R will be used most of the time, with some incursion into Gephi, for large networks visualisations. Knowledge of R is therefore recommended to get the best out of this applied introduction to Social Network Analysis. 

David Easley and Jon Kleinberg, Networks, Crowds and Markets, Cambridge University Press, 2010.

John Scott, Social Network Analysis. A Handbook, Sage, 2005.

While not required, the following references are useful background readings for the course:

Jackson M. (2019), The Human Network, Atlantic Books, London.

de Martí, J. and Y. Zenou (2011), “Social networks” In: I. Jarvie and J. Zamora-Bonilla (Eds.), Handbook of Philosophy of Social Science, London: SAGE Publications, Chap. 16, 339-361.

De Benedictis L. and L. Tajoli (2011), “The World Trade Network.” The World Economy, 34, 8, 1417-1454.

• Lessons (with and without slides).

  
  

  Data Analysis with R and interpretation of network and relational data.

• Weakly check of students application of network analysis in the R lab.

  
  

  Final Multiple choice written exam.

  
  

  The final test is composed of 21 questions, with 4 answers each, of which only one is correct. Each correct answer ensures 1.5 points. Answers not given provide for an evaluation of 0 points, while incorrect ones provide for a penalty of -0.5 points.

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