| Name | regrank JSON |
| Version |
0.2.30
JSON |
| download |
| home_page | https://github.com/junipertcy/regrank |
| Summary | Regularized methods for efficient ranking in networks |
| upload_time | 2024-06-30 06:41:48 |
| maintainer | None |
| docs_url | None |
| author | Tzu-Chi Yen |
| requires_python | !=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*,!=3.9.*,>=3.10 |
| license | LGPL-3.0-or-later |
| keywords |
network-analysis
convex-optimization
ranking-algorithm
proximal-gradient-method
denoising
|
| VCS |
 |
| bugtrack_url |
|
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No requirements were recorded.
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No Travis.
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No coveralls.
|
[](https://github.com/junipertcy/regrank/blob/main/LICENSE) [](https://pypi.org/project/regrank/) [](https://pypi.org/project/regrank/) [](https://github.com/junipertcy/regrank/actions) [](https://github.com/ellerbrock/open-source-badges/)
**regrank** 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](https://docs.netscied.tw/regrank/index.html).
* General Q&A, ideas, or other things, please visit [Discussions](https://github.com/junipertcy/regrank/discussions).
* Software-related bugs, issues, or suggestions, please use [Issues](https://github.com/junipertcy/regrank/issues).
## Installation
**regrank** is available on PyPI. It also depends on `graph-tool`. We recommend using `conda` to manage packages.
```bash
conda create --name regrank-dev -c conda-forge graph-tool
conda activate regrank-dev
pip install regrank
```
## Example
```python
# Import the library
import regrank as rr
# Load a data set
g = rr.datasets.us_air_traffic()
# Create a model
model = rr.optimize.regrank(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 = rr.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.
## Data sets
We have a companion repo—[regrank-data](https://github.com/junipertcy/regrank-data)—for data sets used in the paper. Which are:
* [PhD_exchange](https://github.com/junipertcy/regrank-data/tree/main/PhD_exchange)
* [Parakeet](https://github.com/junipertcy/regrank-data/tree/main/parakeet)
In addendum, we have provided the [regrank.datasets](https://junipertcy.github.io/regrank/datasets.html) submodule to load data sets hosted by other repositories, such as the [Netzschleuder](http://networkrepository.com/). See the docs for more information.
## Development
The library uses pytest to ensure correctness. The test suite depends on [mosek](https://www.mosek.com/) and [gurobi](https://www.gurobi.com/).
## License
**regrank** is open-source and licensed under the [GNU Lesser General Public License v3.0](https://www.gnu.org/licenses/lgpl-3.0.en.html).
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