The course focuses on the mathematical and statistical analysis of social network data. With topics such as big data and data science becoming increasingly popular, the study of large datasets of networks (or graphs), is becoming increasingly important. Examples of such networks include email communication logs, communication and collaboration networks, and relationships on online social network platforms (such as Facebook and Twitter). Moreover, recent technology allows for researchers to generate network data with unprecedented resolution. For example, sociometric badges, location sensors in cell phones, smart cameras, or access passes can all generate data on who is where when with whom, enabling researchers to follo social networks in real time at scale.
Example applications that may be discussed include health behavior (e.g., how being or becomeing overweight is related to ones network position or how smoking behavior is a networked phenomenon), spreading of diseases, social influence (e.g., political preference of individuals or voting behavior of legislators), friendship choices, criminal behavior, between-country hostility, marketing (ie. how to use knowledge of networks to make people buy a product), and organizational behavior (e.g., how teams in organizations function).
The following topics are at the core of the course:
- introduction to social networks: what are social networks, approaches to social network analysis (esp. the engineering approach versus the sociological approach), history of social network analysis, applications of social networks;
- data manipulation: combining multiple datasets and transforming them into data structures that can be analyzed using social network analysis tools;
- network measures: measures that describe individual position (e.g., centrality, status, influence, bridges) and the structure of the whole network (e.g., communities, subgroups, centralization, fragility, reach, core-periphery);
- static and interactive network visualization;
- data collection techniques (including surveys, sensors, online data);
- statistical analysis of cross-sectional networks (e.g., ERGM's);
- statistical analysis of longitudinal network data (e.g., Relational Event Models).
The course will use R as its primary analysis environment. Although some other programming enviroments have pretty good social network analysis tools, the power and extensiveness of social network analysis facilities for R are unparallelled, especially with respect to their statistical modeling. We will assume a working knowledge of the R language but we will also spend time on the coding skills to process and analyze network data in R.
The course will combine lectures with labwork and several group assignments. In the lectures, the material is introduced and explained. In the labs, students will become familiar with the methods by working with them in a supervised setting. Throughout the course, students will work on a few group assignments that require them to apply their skills and understanding to perform analyses themselves without supervision. These assignments will be graded.
Filippo Menczer (Author), Santo Fortunato (Author), Clayton A. Davis - 2020 - A First Course in Network Science, Cambridge University Press. ISBN-13: 978-1108471138/ISBN-10: 1108471137.
The book is supplemented with papers from academic journals.
It is recommended that students have introductory-level knowledge of programming and data structures.
They are expected to have a reasonable level of expertise with R and fluency in inferential statistics.
Knowledge of Bayesian statistics is welcomed, but not necessary.
The course grade will be a combination of the grades for the group assignments and a final exam (individual). The exam will consist of several open questions (that test students understanding and knowledge of the material) and a data analysis assignment where students will conduct analyses of a dataset provided to them in the exam. The exam will be conducted through the Tilburg University TestVision e-assessment software.
TYPE OF INSTRUCTION
Lectures and computer labs
|Written test opportunities|
|Written test opportunities (HIST)|
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