Hard Sample Aware Network

<p align="center"> <a href="https://pytorch.org/" alt="PyTorch"> <img src="https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?e&logo=PyTorch&logoColor=white" /></a> <a href="https://aaai.org/Conferences/AAAI-23/" alt="Conference"> <img src="https://img.shields.io/badge/AAAI'23-brightgreen" /></a> <p/>

An official source code for paper Hard Sample Aware Network for Contrastive Deep Graph Clustering, accepted by AAAI 2023. Any communications or issues are welcomed. Please contact yueliu19990731@163.com. If you find this repository useful to your research or work, it is really appreciate to star this repository. :heart:

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Overview

<p align = "justify"> We propose a novel contrastive deep graph clustering method dubbed Hard Sample Aware Network (HSAN) by introducing a comprehensive similarity measure criterion and a general dynamic sample weighing strategy. Concretely, in our algorithm, the similarities between samples are calculated by considering both the attribute embeddings and the structure embeddings, better-revealing sample relationships and assisting hardness measurement. Moreover, under the guidance of the carefully collected high-confidence clustering information, our proposed weight modulating function will first recognize the positive and negative samples and then dynamically up-weight the hard sample pairs while down-weighting the easy ones. In this way, our method can mine not only the hard negative samples but also the hard positive sample. <div align="center"> <img src="./assets/HSAN_model.png" width=80%/> </div> <div align="center"> Figure 1: Illustration of the proposed Hard Sample Aware Network (HSAN). </div>

Requirements

The proposed HSAN is implemented with python 3.7 on a NVIDIA 3090 GPU.

Python package information is summarized in requirements.txt:

• torch==1.7.1
• tqdm==4.59.0
• numpy==1.19.2
• munkres==1.1.4
• scikit_learn==1.2.0

Quick Start

• Step1: use the cora.zip file or download other datasets from Awesome Deep Graph Clustering/Benchmark Datasets

• Step2: unzip the dataset into the ./dataset folder

• Step3: run

  python train.py
  

  the clustering results will be recorded in the ./results.csv file

Parameter settings

<div align="center"> <img src="./assets/HSAN_parameter.png" width=70%/> </div> <div align="center"> Table 1: Parameter settings of six datasets. </div>

Clustering Results

<div align="center"> <img src="./assets/HSAN_result.png" width=100%/> </div> <div align="center"> Table 2: Clustering results of our proposed HSAN and thirteen baselines on six datasets. </div> <div align="center"> <img src="./assets/HSAN_tsne.png" width=100%/> </div> <div align="center"> Figure 2: 2D <i>t</i>-SNE visualization of seven methods on two datasets. </div>

Citation

If you find this project useful for your research, please cite your paper with the following BibTeX entry.

@inproceedings{HSAN,
  title={Hard Sample Aware Network for Contrastive Deep Graph Clustering},
  author={Liu, Yue and Yang, Xihong and Zhou, Sihang and Liu, Xinwang and Wang, Zhen and Liang, Ke and Tu, Wenxuan and Li, Liang and Duan, Jingcan, and Chen, Cancan},
  booktitle={Proc. of AAAI},
  year={2023}
}

@article{Deep_graph_clustering_survey,
 author = {Liu, Yue and Xia, Jun and Zhou, Sihang and Wang, Siwei and Guo, Xifeng and Yang, Xihong and Liang, Ke and Tu, Wenxuan and Li, Z. Stan and Liu, Xinwang},
 journal = {arXiv preprint arXiv:2211.12875},
 title = {A Survey of Deep Graph Clustering: Taxonomy, Challenge, and Application},
 year = {2022}
}