Awesome
KDD2024ProCom
This is the official repo for KDD 2024 Research Track paper ''ProCom: A Few-shot Targeted Community Detection Algorithm''. [Paper]
If you find this work useful in your method, you can cite the paper as below:
@article{wu2024procom,
title={ProCom: A Few-shot Targeted Community Detection Algorithm},
author={Xixi Wu and Kaiyu Xiong and Yun Xiong and Xiaoxin He and Yao Zhang and Yizhu Jiao and Jiawei Zhang},
year={2024},
eprint={2408.07369},
archivePrefix={arXiv},
primaryClass={cs.SI},
url={https://arxiv.org/abs/2408.07369},
}
Run the Codes
Repo Introduction
This repo contains the following contents:
.
├── README.md
├── ckpts --- This folder contains pre-trained models for reproducibility
├── code --- This folder contains codes of ProCom
│ ├── data.py (data loading)
│ ├── metrics.py (evaluation)
│ ├── model.py (GNN encoder and Prompt layer)
│ ├── pretrain.py (context-aware pre-training)
│ ├── run.py (run ProCom pipeline)
│ ├── run_pretrain.py (run ProCom's pretraining phase)
│ └── utils.py (utilization functions)
├── data --- This folder contaisn 5 experimental datasets
│ ├── amazon
│ ├── dblp
│ ├── facebook
│ ├── lj
│ └── twitter
└── logs --- This folder contains several running logs for references
├── AMAZON_EXAMPLE.log
├── DBLP_EXAMPLE.log
└── FACEBOOK_EXAMPLE.log
Environmental Requirements
- Python 3.7 or above
- Install pytorch with version 1.13.0 or later
- Install Pytorch-Geometric (PyG) with version 2.3.1. Please refer to PyG official website for more information of installing prerequisites.
Running Scripts
Execute code/run_pretrain.py for the pre-training stage and saving the pre-trained model:
python run_pretrain.py --dataset=DATASET_NAME
Execute code/run.py for the overall ProCom pipeline
python run.py --dataset=DATASET_NAME --run_times=YOUR_RUN_TIMES --subg_scale=PARAMETER_LAMBDA
Descriptions of arguments (for more options, please refer to run.py)
--dataset [facebook, amazon, dblp, twitter, lj]: the dataset to run
--num_shot: number of prompt communities, default as 10
--num_pred: number of predicted communities
--gnn_type [GCN, GAT, SAGE, GIN, TransformerConv]: gnn encoder type, default as GCN
--hidden_dim: embedding dimention, default as 128
--node_scale: weight of L_{n2c}, default as 1.0
--subg_scale: weight of L_{c2c}, default as 0.1, search fron {0.001, 0.01, 0.1, 1}
Example Logs
We have uploaded some running logs under the logs folder for reference.
Performance
We conduct the overall performance comparison with both traditional community detection methods (BigClam, ComE, CommunityGAN) and semi-supervised methods (Bespoke, SEAL, CLARE).
For traditional methods, the results are reported from SEAL and CLARE. For semi-supervised methods, we run their released codes under 10-shot setting, and parameters are set following the original papers.
- Bespoke: Semi-Supervised Community Detection Using Structure and Size. In ICDM, 2018. Official Implementation
- SEAL: SEAL: Learning Heuristics for Community Detection with Generative Adversarial Networks. In KDD, 2020. Official Implementation
- CLARE: CLARE: A Semi-supervised Community Detection Algorithm. In KDD, 2022. Official Implementation
| F1-Score | $\text{BigClam}$ | $\text{ComE}$ | $\text{CommunityGAN}$ | $\text{Bespoke}$ | $\text{SEAL}$ | $\text{CLARE}$ | $\text{ProCom (Ours)}$ |
|---|---|---|---|---|---|---|---|
| $32.92$ | $27.92$ | $32.05$ | $29.67_{\pm 0.85}$ | $31.10_{\pm 3.84}$ | $28.53_{\pm 1.36}$ | $\textbf{38.57}_{\pm 2.02}$ | |
| Amazon | $53.79$ | $48.23$ | $51.09$ | $80.38_{\pm 0.64}$ | $82.26_{\pm 4.04}$ | $78.89_{\pm 2.10}$ | $\textbf{84.36}_{\pm 0.23}$ |
| Livejournal | $39.17$ | $\text{N/A}$ | $40.67$ | $30.98_{\pm 1.55}$ | $42.85_{\pm 2.60}$ | $45.38_{\pm 4.07}$ | $\textbf{54.35}_{\pm 3.04}$ |
| DBLP | $40.41$ | $25.24$ | $\text{N/A}$ | $41.55_{\pm 0.40}$ | $41.74_{\pm 6.35}$ | $48.75_{\pm 2.51}$ | $\textbf{50.96}_{\pm 1.57}$ |
| $24.33$ | $15.89$ | $\text{N/A}$ | $29.85_{\pm 0.15}$ | $16.97_{\pm 1.32}$ | $20.05_{\pm 0.88}$ | $\textbf{31.09}_{\pm 0.35}$ |