This site is a place to host Jupyter Notebooks for the Neurodata lab at Johns Hopkins University.

If you are a Neurodata member with write access and would like to add a notebook, check out the Github repo that builds this site. Simply upload your notebook to the _notebooks folder, following all of the instructions there.

For more information on the lab, visit our about page, or our Github to see other projects and open source software.

This site is built with fastpages, An easy to use blogging platform with extra features for Jupyter Notebooks. fastpages automates the process of creating blog posts via GitHub Actions, so you don’t have to fuss with conversion scripts.
A full list of features can be found on GitHub.

Posts

• Hierarchical partitioning and plotting on the hemibrain connectome

  Here I demonstrate a simple hierarchical partitioner and how we can plot these results

  Jun 3, 2021

• Generating a graph layout for the Hemibrain data

  Here I use some of our layout tools to plot a 2D representation of the Hemibrain Fly Connectome.

  May 6, 2021

• Matching ALPNs

  Exploring the use of quadradic assignment problem solvers to match neuron morphologies.

  Mar 19, 2021

• The Kavli-ome

  Plotting academic connections for the Kavli Neuroscience Discovery Institute at Johns Hopkins University.

  Mar 18, 2021

• Ranking teams by minimizing upsets via graph matching

  Here we frame the ranking of teams in a network of sports results as a quadradic assignment/graph matching problem.

  Mar 5, 2021

• Graph matching with "Light Speed Sinkhorn Distances"

  Simulated data experiments showing the effectiveness of graph matching using sinkhorn to calculate search direction

  Feb 25, 2021

• Embedding graphs with covariates

  Some experiments on a real dataset of interest using CASE and MASE

  Nov 17, 2020

• Finding the right categorical labeling

  Here I show how to resolve the nonidentifiability in categorical labelings

  Oct 12, 2020

• Modeling the distribution of neural networks

  An attempt to characterize the distribution of neural network parameters that are learned on the same task

  Oct 5, 2020

• Simple random graph models in the latent space framework

  Here we investigate the connection between the ER, SBM, and DCSBM models in the latent space framework

  Sep 24, 2020

• Shuffling Alters QAP Results

  Simulated data experiments showing the effect of shuffling the B matrix in FAQ

  Aug 31, 2020

• Can you make the objective function better via gm, compared to the purported 1-1?

  Running GM on time stamped data with a known 1-1 correspondance

  Aug 29, 2020

• GM+SS using JAgt Seedless Procrustes

  Simulated data experiments showing the effectiveness of graph matching with spectral similarity

  Aug 24, 2020