Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
2 credits
15.0 h
Q2
Teacher(s)
Heeren Alexandre;
Language
English
Content
Graph theory and network analysis have recently started to infiltrate psychology and neuroscience, especially in research programs dealing with huge data sets and connectivity issues.
Accordingly, this course will provide a general overview of graph theory and network analysis. Illustrations on real data sets will be provided throughout the workshop. Given the diversity of the audience, examples will be ranging from the study of social networks to brain networks and symptoms connectivity.
Course participants will:
- become familiar with general notions of graph theory and network analysis
- learn how to model network data using R, to implement algorithms from the field of graph theory (e.g., community detection, smallwordness), and to use tools from data science (e.g., graphical Lasso) to optimize network estimation and visualization
- understand the advantages, challenges, and limitations of network analysis in comparison to other analytical approaches
- and become able to critically assess papers dealing with network analysis and graph theory in the field of psychology and neuroscience.
Accordingly, this course will provide a general overview of graph theory and network analysis. Illustrations on real data sets will be provided throughout the workshop. Given the diversity of the audience, examples will be ranging from the study of social networks to brain networks and symptoms connectivity.
Course participants will:
- become familiar with general notions of graph theory and network analysis
- learn how to model network data using R, to implement algorithms from the field of graph theory (e.g., community detection, smallwordness), and to use tools from data science (e.g., graphical Lasso) to optimize network estimation and visualization
- understand the advantages, challenges, and limitations of network analysis in comparison to other analytical approaches
- and become able to critically assess papers dealing with network analysis and graph theory in the field of psychology and neuroscience.
Teaching methods
Workshop
Evaluation methods
Oral presentations + assessment via exercices on real data.
Online resources
Handouts as well as examples of programming codes will be made available via Moodle.
Bibliography
A list of reading articles will be provided at the end of each session.
Faculty or entity
EPSY
