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.

Topics include:

  • 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.



Workshop slides are available for download here.


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:

Python3 (required)

Matplotlib (required)

Networkx (required)

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

Lab Materials:

A pre-compiled version of the lab is available online.

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!

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