Network Analysis: Overview and Applications To Population Science
Ceren Budak and Daniel Romero
June 4th, 2019
The PDHP workshop series resumes with our first workshop of the summer: Network Analysis: Overview and Applications To Population Science. Please join instructors Ceren Budak and Daniel Romero (both from U of M School of Information and formerly Microsoft Research) for a half-day workshop geared toward population researchers and data scientists of all experience levels. The workshop features 2 hours of lecture (covering fundamental principles and theory of network analysis) followed by 2 hours of lab (simulation-based information diffusion within networks and optimal seed node selection), while exploring the connections between network analysis and social research.
- Basic concepts of networks and network data
- Measuring network properties such as centrality and node/edge importance
- Various models of information diffusion and cascade effects
- Network-based classification methods (including Random Walk and K-nearest neighbors)
- Network simulation using Python
- Impact of seed node selection on network properties.
The lab portion of this workshop is conducted using Python 3 and Jupyter Notebooks, using two Python modules (matplotlib and networkx). Install links for the software are below:
–Jupyter Notebook (strongly recommended, but other Notebooks/IDEs should also work)
If using the pip command, you can install with 4 lines of code:
python -m pip install --upgrade pip python -m pip install -U matplotlib python -m pip install -U networkx python -m pip install -U jupyter
Materials for the full Jupyter Notebook version of the lab (including the notebook file, 2 toy datasets used in the lab, and an additional sourcecode file are available. NOTE: The lab code assumes all 4 zipped files are stored in the same directory.
Thank you for your interest in our PDHP data workshops!