regrank implements a suite of regularized models to infer the hierarchical structure in a directed network.
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This is the software repository behind the paper:
- Tzu-Chi Yen and Stephen Becker, Regularized methods for efficient ranking in networks, in preparation.
RegRank relies on Ax for hyperparameter search. If you are using Mac, according to Ax's docs, we recommend you install PyTorch manually before installing Ax, using the Anaconda package manager.
conda create --name regrank-dev -c conda-forge graph-tool
conda activate regrank-dev
conda install pytorch torchvision -c pytorch # OSX only (details below)
pip install regrank# Import the library
import regrank as rr
# Load a data set
g = rr.datasets.us_air_traffic()
# Create a model
model = rr.SpringRank(method="annotated")
# Fit the model: We decided to analyze the `state_abr` nodal metadata,
# We may inspect `g.list_properties()` for other metadata to analyze.
result = model.fit(g, alpha=1, lambd=0.5, goi="state_abr")
# Now, result["primal"] should have the rankings. We can compute a summary.
summary = model.compute_summary(g, "state_abr", primal_s=result["primal"])Let's plot the rankings, via rr.plot_hist(summary). Note that most of the node categories are regularized to have the same mean ranking.
We provided a summary via rr.print_summary_table(summary).
+-------+-------+--------+-----------------------------------------+--------+---------+
| Group | #Tags | #Nodes | Members | Mean | Std |
+-------+-------+--------+-----------------------------------------+--------+---------+
| 1 | 5 | 825 | CA, WA, OR, TT, AK | 0.047 | 1.1e-02 |
| 2 | 4 | 206 | TX, MT, PA, ID | -0.006 | 4.2e-03 |
| 3 | 43 | 1243 | MI, IN, TN, NC, VA, IL, CO, WV, MA, WI, | -0.035 | 4.3e-03 |
| | | | SC, KY, MO, MD, AZ, PR, LA, UT, MN, GA, | | |
| | | | MS, HI, DE, NM, ME, NJ, NE, VT, CT, SD, | | |
| | | | IA, NV, ND, AL, OK, AR, NH, RI, OH, FL, | | |
| | | | KS, NY, WY | | |
| 4 | 1 | 4 | VI | -0.072 | 0.0e+00 |
+-------+-------+--------+-----------------------------------------+--------+---------+
The result suggests that states such as CA, WA, or AK are significantly more popular than other states.
We have a companion repo, regrank-data, which stores the data sets used in the paper. These data can be loaded via the regrank.datasets submodule, and will load into a graph-tool graph object. See the docs for more description.
- PhD_exchange
- Parakeet
- Austrian internal migrations: Network of migrations between municipalities in Austria, from 2002 to 2022.
- U.S. air traffic: Yearly snapshots of flights among all commercial airports in the United States from 1990 to today.
The library uses pytest to ensure correctness. The test suite uses CVXPY's SCS solver to compare results. One may optionally use other solvers but they must be installed independently. See their installation guide. Use pre-commit run --all-files for pre-commit checks.
regrank is open-source and licensed under the GNU Lesser General Public License v3.0.