rSpringRank 0.2.28

Regularized methods for efficient ranking in networks

rSpringRank implements a collection of regularized, convex models (+solvers) that allow the inference of hierarchical structure in a directed network, which exists due to dominance, social status, or prestige. Specifically, this work leverages the time-varying structure and/or the node metadata present in the data set.

This is the software repository behind the paper:

• Tzu-Chi Yen and Stephen Becker, Regularized methods for efficient ranking in networks, in preparation.
• For full documentation, please visit this site.
• General Q&A, ideas, or other things, please visit Discussions.
• Software-related bugs, issues, or suggestions, please use Issues.

Installation

rSpringRank is available on PyPI. It also depends on graph-tool. We recommend using conda to manage packages.

conda create --name rSpringRank-dev -c conda-forge graph-tool
conda activate rSpringRank-dev
pip install rSpringRank

Example

# Import the library
import rSpringRank as sr

# Load a data set
g = sr.datasets.us_air_traffic()

# Create a model
model = sr.optimize.rSpringRank(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 = sr.compute_summary(g, "state_abr", primal_s=result["primal"])

Let's plot the rankings, via sr.plot_hist(summary). Note that most of the node catetories are regularized to have the same mean ranking.

We provided a summary via sr.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.

Data sets

We have a companion repo—rSpringRank-data—for data sets used in the paper. Which are:

• PhD_exchange
• Parakeet

In addendum, we have provided the rSpringRank.datasets submodule to load data sets hosted by other repositories, such as the Netzschleuder. See the docs for more information.

Development

The library uses pytest to ensure correctness. The test suite depends on mosek and gurobi.

License

rSpringRank is open-source and licensed under the GNU Lesser General Public License v3.0.