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# [PANINIpy](https://github.com/baiyueh/PANINIpy)
PANINIpy: Package of Algorithms for Nonparametric Inference with Networks in Python is a package designed for nonparametric inference with complex network data, with methods for identifying hubs in networks, regionalizing mobility or distributional data over spatial networks, clustering network populations, and constructing hypergraphs from temporal data among other features.
## Table of Contents
- [Installation](#installation)
- [Modules](#modules)
- [Binning Temporal Hypergraphs](#binning-temporal-hypergraphs)
- [Clustering Network Populations](#clustering-network-populations)
- [Regionalization with Distributional Data](#regionalization-with-distributional-data)
- [Identifying Network Hubs](#identifying-network-hubs)
- [Regionalization with Community Detection](#regionalization-with-community-detection)
- [MDL Network Backbones](#mdl-network-backbones)
- [Documentation](#documentation)
- [License](#license)
## Installation
pip install paninipy
### [PyPI](https://pypi.org/project/paninipy/)
## Modules
### [Binning Temporal Hypergraphs](https://paninipy.readthedocs.io/en/latest/Papers/hypergraph_binning.html)
Identify MDL-optimal temporally contiguous partitions of event data between distinct node sets (e.g. users and products).\
Utilizes method derived in “Inference of dynamic hypergraph representations in temporal interaction data” (Kirkley, 2024, https://arxiv.org/abs/2308.16546).
### [Clustering Network Populations](https://paninipy.readthedocs.io/en/latest/Papers/population_clustering.html)
Generate synthetic network population datasets and perform clustering of observed network populations, multilayer network layers, or temporal networks.\
Utilizes method derived in “Compressing network populations with modal networks reveals structural diversity” (Kirkley et al., 2023, https://arxiv.org/pdf/2209.13827).
### [Regionalization with Distributional Data](https://paninipy.readthedocs.io/en/latest/Papers/distributional_regionalization.html)
Perform MDL-based regionalization on distributional (e.g. census) data over space.\
Utilizes method derived in “Spatial regionalization as optimal data compression” (Kirkley, 2022, https://arxiv.org/pdf/2111.01813).
### [Identifying Network Hubs](https://paninipy.readthedocs.io/en/latest/Papers/hub_identification.html)
Identify hub nodes in a network using different information theoretic criteria.\
Utilizes methods derived in “Identifying hubs in directed networks” (Kirkley, 2024, https://arxiv.org/pdf/2312.03347).
### [Regionalization with Community Detection](https://paninipy.readthedocs.io/en/latest/Papers/community_regionalization.html)
Perform community detection-based regionalization on network data.\
Utilizes method derived in “Bayesian regionalization of urban mobility networks” (Morel-Balbi and Kirkley, 2024, https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033307).
### [MDL Network Backbones](https://paninipy.readthedocs.io/en/latest/Papers/mdl_backboning.html)
Infer global and local backbones of a network using the minimum description length principle .\
Utilizes method derived in “Fast nonparametric inference of network backbones for graph sparsification” (Kirkley, 2024, https://arxiv.org/abs/2409.06417).
## Documentation
Detailed documentation for each module and function is available at the link below:
### [PANINIpy Documentation](https://paninipy.readthedocs.io/en/latest/)
### Attribution
The logo for this package was enhanced using **Stable Diffusion model**, an AI-based generative model created by Robin Rombach, Patrick Esser and contributors.
The model is released under the **CreativeML Open RAIL-M License**. For more details on the model and its licensing, refer to the following:
- [Stable Diffusion Project](https://stability.ai/)
- [CreativeML Open RAIL-M License](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE)
## License
Distributed under the MIT License. See LICENSE for more information.
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"description": "<meta name=\"google-site-verification\" content=\"QBsxqO0wWO-ZZl0YBi0pC_PCSEgF9Z671lLwrUavwyE\" />\r\n<img src=\"https://imgur.com/RPhHG5W.png\" width=\"275px\">\r\n\r\n[](https://pypi.org/project/paninipy/?kill_cache=1)\r\n[](https://paninipy.readthedocs.io/en/latest/)\r\n[](https://baiyueh.github.io/PANINIpy/)\r\n[](https://github.com/baiyueh/PANINIpy/actions/workflows/auto-test.yml)\r\n\r\n# [PANINIpy](https://github.com/baiyueh/PANINIpy)\r\n\r\nPANINIpy: Package of Algorithms for Nonparametric Inference with Networks in Python is a package designed for nonparametric inference with complex network data, with methods for identifying hubs in networks, regionalizing mobility or distributional data over spatial networks, clustering network populations, and constructing hypergraphs from temporal data among other features. \r\n\r\n## Table of Contents\r\n\r\n- [Installation](#installation)\r\n- [Modules](#modules)\r\n - [Binning Temporal Hypergraphs](#binning-temporal-hypergraphs)\r\n - [Clustering Network Populations](#clustering-network-populations)\r\n - [Regionalization with Distributional Data](#regionalization-with-distributional-data)\r\n - [Identifying Network Hubs](#identifying-network-hubs)\r\n - [Regionalization with Community Detection](#regionalization-with-community-detection)\r\n - [MDL Network Backbones](#mdl-network-backbones)\r\n- [Documentation](#documentation)\r\n- [License](#license)\r\n\r\n## Installation\r\n\r\npip install paninipy\r\n\r\n### [PyPI](https://pypi.org/project/paninipy/)\r\n\r\n## Modules\r\n### [Binning Temporal Hypergraphs](https://paninipy.readthedocs.io/en/latest/Papers/hypergraph_binning.html)\r\n\r\nIdentify MDL-optimal temporally contiguous partitions of event data between distinct node sets (e.g. users and products).\\\r\nUtilizes method derived in \u201cInference of dynamic hypergraph representations in temporal interaction data\u201d (Kirkley, 2024, https://arxiv.org/abs/2308.16546).\r\n\r\n\r\n### [Clustering Network Populations](https://paninipy.readthedocs.io/en/latest/Papers/population_clustering.html)\r\n\r\nGenerate synthetic network population datasets and perform clustering of observed network populations, multilayer network layers, or temporal networks.\\\r\nUtilizes method derived in \u201cCompressing network populations with modal networks reveals structural diversity\u201d (Kirkley et al., 2023, https://arxiv.org/pdf/2209.13827).\r\n\r\n### [Regionalization with Distributional Data](https://paninipy.readthedocs.io/en/latest/Papers/distributional_regionalization.html)\r\n\r\nPerform MDL-based regionalization on distributional (e.g. census) data over space.\\\r\nUtilizes method derived in \u201cSpatial regionalization as optimal data compression\u201d (Kirkley, 2022, https://arxiv.org/pdf/2111.01813).\r\n\r\n### [Identifying Network Hubs](https://paninipy.readthedocs.io/en/latest/Papers/hub_identification.html)\r\n\r\nIdentify hub nodes in a network using different information theoretic criteria.\\\r\nUtilizes methods derived in \u201cIdentifying hubs in directed networks\u201d (Kirkley, 2024, https://arxiv.org/pdf/2312.03347).\r\n\r\n\r\n### [Regionalization with Community Detection](https://paninipy.readthedocs.io/en/latest/Papers/community_regionalization.html)\r\n\r\nPerform community detection-based regionalization on network data.\\\r\nUtilizes method derived in \u201cBayesian regionalization of urban mobility networks\u201d (Morel-Balbi and Kirkley, 2024, https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.6.033307).\r\n\r\n### [MDL Network Backbones](https://paninipy.readthedocs.io/en/latest/Papers/mdl_backboning.html)\r\n\r\nInfer global and local backbones of a network using the minimum description length principle .\\\r\nUtilizes method derived in \u201cFast nonparametric inference of network backbones for graph sparsification\u201d (Kirkley, 2024, https://arxiv.org/abs/2409.06417).\r\n\r\n## Documentation \r\n\r\nDetailed documentation for each module and function is available at the link below:\r\n### [PANINIpy Documentation](https://paninipy.readthedocs.io/en/latest/)\r\n\r\n\r\n### Attribution\r\nThe logo for this package was enhanced using **Stable Diffusion model**, an AI-based generative model created by Robin Rombach, Patrick Esser and contributors. \r\n\r\nThe model is released under the **CreativeML Open RAIL-M License**. For more details on the model and its licensing, refer to the following:\r\n\r\n- [Stable Diffusion Project](https://stability.ai/)\r\n- [CreativeML Open RAIL-M License](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE)\r\n\r\n## License \r\nDistributed under the MIT License. See LICENSE for more information.\r\n",
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