Jean-Gabriel Young

This pages contains the references that I gathered while preparing my lecture for the 2016 CRM Summer School, which took place in Québec, Canada.

Material of the talk

• ipython notebook (python 3)

• slides

• lecture notes [in revision]

Resources

Papers

• Tutorial on Spectral Clustering by Ulrike von Luxburg (2007)

• Continuous maximization point of view of graph clustering (M. A. Riolo and M.E.J. Newman, 2014)

• Equivalent paper for the modularity matrix (P. Zhang and M.E.J. Newman, 2015)

• Shape of the spectrum: Graph Bisection of the planted partition model [Random matrix theory] (R.R. Nadakuditi and M.E.J. Newman, 2012)

• Shape of the spectrum: Graph clustering of the stochastic block model [Random matrix theory] (T. Peixoto 2013)

Software

• networkx: Simple graph module in pure python. It comes with multiple functions and methods out-of-the-box.

• graph_tool: Graph module for python, in cython. Favors the statistical approach, but the module includes some spectral methods. It’s fast.

• scikit-learn: Machine learning in python. Include spectral methods, k-means, and more.

• seaborn: visualization. Straigtforward visualization module, perfect for spectral density plots.