This repository hosts code related to the Temporal Mapper project written by Mengsen Zhang. The associated paper is the following:
Zhang, M., Chowdhury, S., & Saggar, M. (2022). Temporal Mapper: transition networks in simulated and real neural dynamics. Network Neuroscience.
Here's a schematic (Fig. 3 from the paper) showing how Temporal Mapper operates on a dataset.
To run the code, you only need a (time_frames x nodes) matrix! Try starting with the code provided in run_sim.m. You should get the following image:
When running with your own data, you only need to supply two parameters:
- k: locality parameter for kNN graph construction
- delta: distance parameter for performing asymmetric graph compression
We have provided some helper files to help you generate additional trials of simulated data. See the script in code/gen_sim.m and also the original paper for more details. If everything runs fine, you should see the following image:
The core Temporal Mapper code has been tested on Matlab R2020b.
To facilitate comparison of transition networks of different sizes, we also provide a GPU implementation of a variant of the Gromov-Wasserstein distance. Please see the readme inside the GWTLBgpu folder for more details.