Awesome

Hard Sample Aware Network

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:

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}
}