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LICENSE

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+MIT License
+
+Copyright (c) 2023 Nicolò Ruggeri, Anna Badalyan
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+<h1 align="center">
+HyCoSBM <br/>  
+<i>Hypergraph Covariate Stochastic Block Model</i>
+</h1>
+
+<p align="center">
+<i>Probabilistic model on hypergraphs able to incorporate the information about node covariates. </i>
+</p>
+
+<p align="center">
+<a href="https://github.com/badalyananna/HyCoSBM/blob/main/LICENSE" target="_blank">
+<img alt="License: MIT" src="https://img.shields.io/github/license/badalyananna/HyCoSBM">
+</a>
+
+<a href="https://www.python.org/" target="_blank">
+<img alt="Made with Python" src="https://img.shields.io/badge/made%20with-python-1f425f.svg">
+</a>
+
+<a href="https://github.com/psf/black" target="_blank">
+<img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg">
+</a>
+
+<a href="http://arxiv.org/abs/2311.03857" target="_blank">
+<img alt="ARXIV: 2311.03857" src="https://img.shields.io/badge/arXiv-2311.03857-red.svg">
+</a>
+
+</p>
+
+This repository contains the implementation of the <i>HyCoSBM</i> model presented in:
+
+&nbsp;&nbsp; 
+[1] <i> Hypergraphs with node attributes: structure and inference. </i><br/>
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 
+Anna Badalyan, Nicolò Ruggeri, and Caterina De Bacco<br/>
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 
+[
+    <a href="http://arxiv.org/abs/2311.03857" target="_blank">ArXiv</a>
+]
+
+<i>HyCoSBM</i> is a stochastic block model for higher-order interactions that can 
+incorporate node covariates for improved inference. <br/> 
+This code is made available for the public, if you make use of it please cite our work 
+in the form of the references above.
+The implementation is based on the <a href="https://github.com/nickruggeri/Hy-MMSBM"> Hy-MMSBM </a> model.
+
+
+<h2>Code installation</h2>
+
+The code was developed utilizing <b>Python 3.9</b>, and can be downloaded and used locally as-is. <br>
+To install the necessary packages, run the following command
+
+`pip install -r requirements.txt`
+
+
+<h2>Inference of community structure</h2>
+
+The inference of the affinity matrix <i>w</i> and community assignments <i>u</i> is 
+performed by running the code in `main_inference.py`. 
+
+The most basic run only needs a hypergraph, the number of communities <i>K</i>, and a path to store the results. <br/>
+For example, to perform inference on the High  School dataset with <i>K=2</i> 
+communities, one can run the following command:
+
+```
+python main_inference.py 
+--K 2 --out_dir ./out_inference --pickle_file data/examples/high_school_dataset/hypergraph.pkl
+```
+
+The basic run, however, doesn't use the attributes. To add the attributes we need to specify the link to a csv file containing attributes with `--attribute_file` parameter and the names of the columns to be used as attributes in `--attribute_names`. By default, `gamma = 0.0`, we can also change this parameter by using `--gamma 0.8` command. The following command runs inference on High School dataset using attributes class and sex with `K = 2` and `gamma = 0.8`.
+``` 
+python main_inference.py 
+--K 2 
+--gamma 0.8
+--out_dir ./out_inference 
+--pickle_file data/examples/high_school_dataset/hypergraph.pkl
+--attribute_file data/examples/high_school_dataset/attributes.csv
+--attribute_names class sex
+```
+
+<h3>Input dataset format</h3>
+
+It is possible to provide the input dataset in two formats.
+
+__1. Text format__<br/> 
+A hypergraph can be provided as input via two *.txt* files,
+containing the list of hyperedges, and the relative weights. 
+This allows the user to provide arbitrary datasets as inputs. 
+To perform inference on a dataset specified in text format, provide the path to the two 
+files as 
+```
+python main_inference.py 
+--K 2 
+--out_dir ./out_inference 
+--hyperedge_file data/examples/high_school_dataset/hyperedges.txt 
+--weight_file data/examples/high_school_dataset/weights.txt
+```
+
+__2. Pickle format__<br/>
+Alternatively, one can provide a `Hypergraph` instance, which is the main representation 
+utilized internally in the code (see `src.data.representation`), serialized via the 
+<a href="https://docs.python.org/3/library/pickle.html">pickle</a> Python library. <br/>
+An example equivalent to the above is
+```
+python main_inference.py 
+--K 2 
+--out_dir ./out_inference 
+--pickle_file data/examples/high_school_dataset/hypergraph.pkl
+```
+Similarly to the text format, this allows to provide arbitrary hypergraphs as input.
+
+<h3>Additional options</h3>
+
+Additional options can be specified, the full documentation is shown by running
+
+`python main_inference.py --help`
+
+Among the important ones we list:
+- `--assortative` whether to run inference with a diagonal affinity matrix <i>w</i>.  
+- `--max_hye_size` to keep only hyperedges up to a given size for inference. If `None`, all hyperedges are utilized.
+- `--w_prior` and `--u_prior` the rates for the exponential priors on the parameters. A value of zero is equivalent to no prior, any positive value is utilized for MAP inference. <br/>
+For non-uniform priors, the path to a file containing a NumPy array can be specified, which will be loaded via `numpy.load`.
+- `--em_rounds` number of EM steps during optimization. It is sometimes useful when the model doesn't converge rapidly.
+- `--training_rounds` the number of models to train with different random initializations. The one with the highest log-likelihood is returned and saved.
+- `--seed` integer random seed.
+
+<h2>Data release</h2>
+
+All synthetically generated attributes and hypergraphs used in the experiments are available in `data/generated` folder.
+
+All real datasets used in the experiments are publically available.