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pdfc's Introduction

Prerequisite-driven Fair Clustering on Heterogeneous Information Networks

This repository contains a PyTorch implementation of PDFC

Requirements

The code has been tested running under Python 3.8.8 with the following packages installed (along with their dependencies):

numpy==1.20.1
pandas==1.2.4
scipy==1.6.2
torch==1.12.0

Data information

The datasets/ contains three real-world datasets, MOOCCube, DBLP, and Movielens.

MOOCCube: this is an online education dataset, and it was collected and published on XuetangX.

DBLP: this is a citation network dataset, and it was collected and published on AMiner.

Movielens: this is a rating dataset about movies collected by GroupLens.

We describe the contents of each dataset file.

features/: This contains the features information of nodes.

path_info/: This contains the number of neighbors of nodes in the adjacency matrix.

prerequisite/: This contains the prerequisite relations between entities.

sensitive/: This contains the sensitive attribute information of nodes.

Repository Organization

main.py: The main running program of PDFC.

models.py: PDFC learning node embeddings based on graph model.

metrics.py: Metrics for evaluating fair clustering.

utils.py: Loading data and data processing.

Running PDFC

To achieve fair clustering, please run

python main.py

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