• TIP-GNN
  • Setup the environment
  • Data Preprocess
  • Run the Script
    • Temporal Link Prediction (Transductive)
    • Temporal Link Prediction (Inductive)
    • Temporal Node Classification
  • Cite us

TIP-GNN

Authors: Tongya Zheng, Zunlei Feng, Tianli Zhang, Yunzhi Hao, Mingli Song, Xingen Wang, Xinyu Wang, Ji Zhao, Chun Chen

Code for Transition Propagation Graph Neural Networks for Temporal Networks.

Setup the Environment

• conda create -n tip python=3.9 -y

• pip install -r requirements.txt

• My torch version is torch-1.10.2+cu113

Data Preprocess

We have preprocessed most temporal graphs in the data/format_data directory, and placed the JODIE datasets at Google drive, which can be downloaded and placed at the data/format_data.

bash init.sh

We use init.sh to make necessary directories for our experiments to store generated datasets by data/*, boost the training speed by gumbel_cache and sample_cache, record training details by log, record testing results by results and nc-results, save our trained models by ckpt and saved_models.

python data_unify.py -t datasplit
python data_unify.py -t datalabel

We use -t datasplit to split datasets into the training, validation and testing set according to the ratios.

Run the Script

Temporal Link Prediction (Transductive)

In the setting of transductive temporal link prediction, we use trainable node embeddings.

• python exper_edge_np.py -d fb-forum

Temporal Link Prediction (Inductive)

In the setting of inductive temporal link prediction,, we firstly generate features for each node to perform inductive link prediction.

• python inductive_util.py -d fb-forum

• python inductive_edge_np.py -d fb-forum

Temporal Node Classification

In the setting of temporal node classification prediction, we use the edge features and freeze the node embeddings as all zeros.

Firstly, we have to train a pre-trained link prediction model following TGAT.

• python exper_edge_np.py -d JODIE-wikipedia -t node -f

• python exper_node_np.py -d JODIE-wikipedia -f --balance --binary

Cite us

@article{zheng2022transition,
  title={Transition Propagation Graph Neural Networks for Temporal Networks},
  author={Zheng, Tongya and Feng, Zunlei and Zhang, Tianli and Hao, Yunzhi and Song, Mingli and Wang, Xingen and Wang, Xinyu and Zhao, Ji and Chen, Chun},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2022},
  pages={1--13},
  publisher={IEEE}
}