Research Code
Multiplex Reconstruction
My “Multiplex Reconstruction” project spans a collection of projects and collabotors, truthfully. The code we have utilized, based on manuscript, are available below and once we have completed preliminary research and published a concentrated version of our tools it will be available here as well.
Multilex reconstruction with partial information
The code for the D. Kaiser, S. Patwardhan, and F. Radicchi, Multiplex Reconstruction with Partial Information, Phys. Rev. E 107, 024309 (2023). was written in Python (python3.6+). The code can be found at this GitHub repository.
The code for the D. Kaiser, S. Patwardhan, M. Kim, and F. Radicchi, Reconstruction of multiplex networks via graph embeddings, (2023). was written in Python (python3.10). The code can be found at this GitHub repository.
Hypergraph Clustering
The code for the Mutual Information for Optimal Discrete Compression paper was written in Python (python3.8+). The code will be linked here when peer review has concluded.
Slides
Chalk-talks
Here are slides from various brief, informal talks I have given in the past aimed at teaching new tools/methods for computational science - so-called “chalk-talks.” These often had interactive demos done alongside the presentations, so the slides may seem a bit incomplete - please feel free to reach out with any questions!
- Data science in the shell
- Zero to (s)hero
- Data science with Python
- Programmatic data visualization
- (Will be posted soon, check back soon!) Illustrative data visualization
- (Will be posted soon, check back soon!) Why you should use Julia
Other code
I have created a handful of utilities that don’t belong exclusively to one of the project repositories above. You can find links to smaller GitHub repos/Gists below.
Scientific
- (Fixing bugs, come back soon!) TUI for LFR generation (Rust re-write)