This repository contains the code developed as part of the author's doctoral research.
The focus of the project is to apply community detection algorithms to identify different structures in the graph. The main goal is to apply community detection methods to generate hybrid partitions to be used in multilabel classification.
If you use this code in your work or research, please cite the repository as follows:
Gatto, E. C. (2025). Generate Partitions Communities [GitHub Repository]. Retrieved from https://github.com/cissagatto/Generate-Partitions-Communities
Here are the main features of this project:
- Apply sparcifications methods in to the builted graphs from this code https://github.com/cissagatto/GraphMultiLabel
- Apply Community detection methods
- Finding Hybrid partitions in those communities
This repository contains the following main scripts:
- libraries.R: Loads the necessary libraries to run the code.
- utils.R: Helper and preprocessing functions.
- functions.R: Functions for running community detection methods and sparcifications
- communities.R: calls communities detections methods and sparcifications
- run.R: Functions to run experiments and apply community detections.
- gpc.R: script for running experiments.
- config-files.R: Configuration file for parameters.
Important: choose the best method and hybrid partitions is under review (choose.R)
A file called datasets-original.csv must be in the root project directory. This file is used to read information about the datasets and they are used in the code. We have 90 multilabel datasets in this .csv file. If you want to use another dataset, please, add the following information about the dataset in the file:
| Parameter | Status | Description |
|---|---|---|
| Id | mandatory | Integer number to identify the dataset |
| Name | mandatory | Dataset name (please follow the benchmark) |
| Domain | optional | Dataset domain |
| Instances | mandatory | Total number of dataset instances |
| Attributes | mandatory | Total number of dataset attributes |
| Labels | mandatory | Total number of labels in the label space |
| Inputs | mandatory | Total number of dataset input attributes |
| Cardinality | optional | ** |
| Density | optional | ** |
| Labelsets | optional | ** |
| Single | optional | ** |
| Max.freq | optional | ** |
| Mean.IR | optional | ** |
| Scumble | optional | ** |
| TCS | optional | ** |
| AttStart | mandatory | Column number where the attribute space begins * 1 |
| AttEnd | mandatory | Column number where the attribute space ends |
| LabelStart | mandatory | Column number where the label space begins |
| LabelEnd | mandatory | Column number where the label space ends |
| Distinct | optional | ** 2 |
| xn | mandatory | Value for Dimension X of the Kohonen map |
| yn | mandatory | Value for Dimension Y of the Kohonen map |
| gridn | mandatory | X times Y value. Kohonen's map must be square |
| max.neigbors | mandatory | The maximum number of neighbors is given by LABELS -1 |
1 - Because it is the first column the number is always 1.
2 - Click here to get explanation about each property.
To run this experiment you need the X-Fold Cross-Validation files and they must be compacted in tar.gz format. You can download these files, with 10-folds, ready for multilabel dataset by clicking here. For a new dataset, in addition to including it in the datasets-original.csv file, you must also run this code here. In the repository in question you will find all the instructions needed to generate the files in the format required for this experiment. The tar.gz file can be placed on any directory on your computer or server. The absolute path of the file should be passed as a parameter in the configuration file that will be read by gpc.R script. The dataset folds will be loaded from there.
You need to have installed all the Java, Python and R packages required to execute this code on your machine or server. This code does not provide any type of automatic package installation!
You can use the Conda Environment that I created to perform this experiment. Below are the links to download the files. Try to use the command below to extract the environment to your computer:
conda env create -file AmbienteTeste.yaml
See more information about Conda environments here
You can also run this code using the AppTainer container that I'm using to run this code in a SLURM cluster. Please, check this tutorial (in portuguese) to see how to do that.
To run this code you will need a configuration file saved in csv format and with the following information:
| Config | Value |
|---|---|
| Dataset_Path | Absolute path to the directory where the dataset's tar.gz is stored |
| Temporary_Path | Absolute path to the directory where temporary processing will be performed * 1 |
| Graph_Path | Absolute path to the directory where the graphs are |
| Similarity | Must be "jaccard", "rogers" or another similarity measure |
| Sparsification | 1 to run with sparsification, 0 to not apply sparcification. (no self loops) |
| Dataset_Name | Dataset name according to dataset-original.csv file |
| Number_Dataset | Dataset number according to dataset-original.csv file |
| Number_Folds | Number of folds used in cross validation |
| Number_Cores | Number of cores for parallel processing |
| R_clone | 1 to upload the results for cloud, 0 otherwise |
| Save_csv_files | 1 to save csv files |
1 - Use directorys like /dev/shm, tmp or scratch here.
IMPORTANT: knn = 1 and threshold = 0 are the graph without self loops.
You can save configuration files wherever you want. The absolute path will be passed as a command line argument.
This code was develop in RStudio Version 1.4.1106 © 2009-2021 RStudio, PBC "Tiger Daylily" (2389bc24, 2021-02-11) for Ubuntu Bionic Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) QtWebEngine/5.12.8 Chrome/69.0.3497.128 Safari/537.36. The R Language version was: R version 4.1.0 (2021-05-18) -- "Camp Pontanezen" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit).
This code may or may not be executed in parallel, however, it is highly recommended that you run it in parallel. The number of cores can be configured via the command line (number_cores). If number_cores = 1 the code will run sequentially. In our experiments, we used 10 cores. For reproducibility, we recommend that you also use ten cores. This code was tested with the birds dataset in the following machine:
System:
Host: bionote | Kernel: 5.8.0-53-generic | x86_64 bits: 64 | Desktop: Gnome 3.36.7 | Distro: Ubuntu 20.04.2 LTS (Focal Fossa)
CPU:
Topology: 6-Core | model: Intel Core i7-10750H | bits: 64 | type: MT MCP | L2 cache: 12.0 MiB | Speed: 800 MHz | min/max: 800/5000 MHz Core speeds (MHz): | 1: 800 | 2: 800 | 3: 800 | 4: 800 | 5: 800 | 6: 800 | 7: 800 | 8: 800 | 9: 800 | 10: 800 | 11: 800 | 12: 800 |
Then the experiment was executed in a cluster at UFSCar.
To run the code, open the terminal, enter the ~/Generate-Partitions-Communities/example folder, and type
Rscript gpc.R [absolute_path_to_config_file]
Example:
Rscript gpc.R "~/Generate-Partitions-Communities/config-files/j-GpositiveGO.csv"
- This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
- This study was financed in part by the Conselho Nacional de Desenvolvimento Científico e Tecnológico - Brasil (CNPQ) - Process number 200371/2022-3.
- The authors also thank the Brazilian research agencies FAPESP financial support.
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