Code for paper---Hierarchical Label Inference Incorporating Attribute Semantics in Attributed Networks.

HLI:.
│  args.py
│  get_data.py
│  pytorchtools.py
│  requirements.txt
│          
├─data            
│  ├─ACS
│  │      
│  └─IS
│          
└─src
    ├─main.py
    ├─models.py
    └─util.py

1. args.py: args.py is the parameter configuration file, including model parameters and training parameters.
2. get_data.py: This file is used to load the data.
3. pytorchtools.py: This file is used to define the earlystopping mechanism.
4. requirements.txt: Dependencies file.
5. data/：Dataset folder.
6. src/main.py: Main file.
7. src/models.py: Implementation of HLI.
8. src/util.py: Defining various toolkits.

Please install the following packages:

gensim==4.1.0
matplotlib==3.2.2
networkx==2.3
numpy==1.21.6
pandas==1.2.3
scikit-learn==1.0.2
scipy==1.7.3
torch==1.10.1+cu111
torch-cluster==1.5.9
torch-geometric==2.2.0
torch-scatter==2.0.9
torch-sparse==0.6.12
torch-spline-conv==1.2.1
tqdm==4.62.3

You can also simply run:

pip install -r requirements.txt

Due to the file size limitation of github, we uploaded the datasets to Google Drive. Please download the two hash_vector_embeddings_2048.pkl files and place them in the corresponding folders before running the code.

cd src/
python main.py --experiment full --dataset IS
python main.py --experiment full --dataset ACS

cd src/
python main.py --experiment semi --dataset IS
python main.py --experiment semi --dataset ACS

@inproceedings{li2023hierarchical,
  title={Hierarchical Label Inference Incorporating Attribute Semantics in Attributed Networks},
  author={Li, Junliang and Yang, Yajun and Hu, Qinghua and Wang, Xin and Gao, Hong},
  booktitle={2023 IEEE International Conference on Data Mining (ICDM)},
  pages={1091--1096},
  year={2023},
  organization={IEEE}
}