hongluzhou / dhypr

⚡️⚡️⚡️ Winner of CIKM 2022 Best Paper

D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning

This is an official implementation of the CIKM 2022 paper D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning. In this repository, we provide the PyTorch code for training and evaluation as described in the paper. The proposed model Digraph Hyperbolic Networks (D-HYPR) considers the unique node neighborhoods in digraphs with multi-scale neighborhood collaboration in hyperbolic space. D-HYPR respects asymmetric relationships of node-pairs, which is guided by sociopsychology-inspired regularizers.

D-HYPR outperforms SOTA on multiple downstream tasks including node classification, link presence prediction, and link property prediction. Particularly, D-HYPR generates meaningful embeddings in very low dimensionalities.

If you find our repo useful in your research, please use the following BibTeX entry for citation.

@article{zhou2022dhypr,
  title={D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning},
  author={Zhou, Honglu and Chegu, Advith and Sohn, Samuel S and Fu, Zuohui and De Melo, Gerard and Kapadia, Mubbasir},
  journal={Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM 2022)},
  year={2022}
}

Installation

conda env create -f environment.yml

Dataset

Please download the data folder using this link.
data/
├── air
├── blog
├── citeseer
├── cora
├── cora_ml
├── dblp
├── survey
├── wiki
The folds of each dataset are provided. Each fold is a unique train-test split of the dataset.

Train and Evaluation

D-HYPR

The scripts to run D-HYPR on the 8 datasets for node classification, link presence prediction, and link sign prediction are available in dhypr/code/D-HYPR.

Link Presence Prediction

The Air Dataset

To get started with a one-time experiment:

dhypr/code/dhypr$ conda activate dhypr
dhypr/code/D-HYPR$ ./onefold_lp_air.sh 

To obtain the results on all folds with repeated runs (100 runs in total using different seeds):

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ python lp_air.py

The Cora Dataset

To get started with a one-time experiment:

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ ./onefold_lp_cora.sh 

To obtain the results on all folds with multiple runs (100 runs in total using different seeds):

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ python lp_cora.py

The Blog Dataset

To get started with a one-time experiment:

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ ./onefold_lp_blog.sh 

To obtain the results on all folds with multiple runs (100 runs in total using different seeds):

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ python lp_blog.py

The Survey Dataset

To get started with a one-time experiment:

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ ./onefold_lp_survey.sh 

To obtain the results on all folds with multiple runs (100 runs in total using different seeds):

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ python lp_survey.py

The DBLP Dataset

To get started with a one-time experiment:

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ ./onefold_lp_dblp.sh 

To obtain the results on all folds with multiple runs (100 runs in total using different seeds):

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ python lp_dblp.py

Node Classification

The Cora-ML Dataset

To get started with a one-time experiment:

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ ./onefold_nc_cora_ml.sh 

To obtain the results on all folds:

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ python nc_cora_ml.py

The CiteSeer Dataset

To get started with a one-time experiment:

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ ./onefold_nc_citeseer.sh 

To obtain the results on all folds:

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ python nc_citeseer.py

The Wiki Dataset

To get started with a one-time experiment:

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ ./onefold_nc_wiki.sh 

To obtain the results on all folds:

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ python nc_wiki.py

Semi-Supervised Node Classification with Varied Label Rates

The Cora-ML Dataset

To get started with a one-time experiment:

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ ./onefold_vnc_cora_ml.sh 

To obtain the results on all folds:

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ python vnc_cora_ml.py

The CiteSeer Dataset

To get started with a one-time experiment:

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ ./onefold_vnc_citeseer.sh 

To obtain the results on all folds:

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ python vnc_citeseer.py

The Wiki Dataset

To get started with a one-time experiment:

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ ./onefold_vnc_wiki.sh 

To obtain the results on all folds:

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ python vnc_wiki.py

Link Sign Classification (SP)

The Wiki Dataset

To get started with a one-time experiment:

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ ./onefold_sp_wiki.sh 

To obtain the results on all folds:

dhypr/code/D-HYPR$ conda activate dhypr
dhypr/code/D-HYPR$ python sp_wiki.py

Collecting Results

Please use dhypr/code/D-HYPR/collect_result_lp.ipynb to gather the metric result values from the multiple link prediction experiments, and then use dhypr/helper/get_lp_result_avg_std.ipynb to get the average metric result values.

Similarly, dhypr/code/D-HYPR/collect_result_nc.ipynb for node classification, dhypr/code/D-HYPR/collect_result_vnc.ipynb for semi-supervised node classification, and dhypr/code/D-HYPR/collect_result_sp.ipynb for link sign prediction.

Example

The following is an example to demonstrate the entire trainining and evaluation pipeline:

dhypr$ cd code/D-HYPR
dhypr/code/D-HYPR$ conda activate dhypr

Run D-HYPR on Cora Link Prediction on just fold 0:

dhypr/code/D-HYPR$ ./onefold_lp_cora.sh 

Run D-HYPR on Cora Link Prediction (100 times):

1. dhypr/code/D-HYPR$ python lp_cora.py
2. Run collect_result_lp.ipynb
3. cd ../../helper
4. Run get_lp_result_avg_std.ipynb

Baselines

The scripts to run the baselines (MLP, HNN, GCN, GAT, HGCN) on the 8 datasets for node classification, link presence prediction, and link sign prediction are available in dhypr/code/baselines.

The baseline code is organized in a similar fashion as D-HYPR, therefore please refer to the above D-HYPR related train/eval instructions to run the baseline code.

For other baselines such as NERD, ATP and APP, please contact Advith Chegu: [e-mail]

The code of D-HYPR and the code of baselines are heavily based on the following publically available implementations (we sincerely appreciate the authors of these repositories):

• https://github.com/HazyResearch/hgcn
• https://github.com/ydzhang-stormstout/HAT
• https://github.com/flyingtango/DiGCN
• https://github.com/deezer/gravity_graph_autoencoders
• https://github.com/matthew-hirn/magnet
• https://github.com/zhenv5/atp
• https://github.com/AnryYang/APP
• https://git.l3s.uni-hannover.de/khosla/nerd

K-Order Proximity Matrix

If you are interested to obtain the four types of k-order proximity matrix described in the paper, please refer to the scripts in the dhypr/preprocessing folder. Please note that the maximal order K (K_max) is currently set to 2. The four types of k-order proximity matrix of the 8 datasets were already extracted and provided in the data foler.