EAGLE: Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization

This repository is the official implementation of "Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization (EAGLE)" accepted by the 37th Conference on Neural Information Processing Systems (NeurIPS 2023).

           

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0. Abstract

Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribution shifts, which are common in real-world scenarios. As the generation of dynamic graphs is heavily influenced by latent environments, investigating their impacts on the out-of-distribution (OOD) generalization is critical. However, it remains unexplored with the following two challenges: 1) How to properly model and infer the complex environments on dynamic graphs with distribution shifts? 2) How to discover invariant patterns given inferred spatio-temporal environments? To solve these challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE) framework for OOD generalization by modeling complex coupled environments and exploiting spatio-temporal invariant patterns. Specifically, we first design the environment-aware EA-DGNN to model environments by multi-channel environments disentangling. Then, we propose an environment instantiation mechanism for environment diversification with inferred distributions. Finally, we discriminate spatio-temporal invariant patterns for out-of-distribution prediction by the invariant pattern recognition mechanism and perform fine-grained causal interventions node-wisely with a mixture of instantiated environment samples. Experiments on real-world and synthetic dynamic graph datasets demonstrate the superiority of our method against state-of-the-art baselines under distribution shifts. To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective.

1. Requirements

Main package requirements:

• CUDA == 10.1
• Python == 3.8.12
• PyTorch == 1.9.1
• PyTorch-Geometric == 2.0.1

To install the complete requiring packages, use following command at the root directory of the repository:

pip install -r requirements.txt

2. Quick Start

Training

To train the EAGLE, run the following command in the directory ./scripts:

python main.py --mode=train --use_cfg=1 --dataset=<dataset_name>

Explanations for the arguments:

• use_cfg: if training with the preset configurations.
• dataset: name of the datasets. collab, yelp and act are for Table 1, while collab_04, collab_06, and collab_08 are for Table 2.

Evaluation

To evaluate the EAGLE with trained models, run the following command in the directory ./scripts:

python main.py --mode=eval --use_cfg=1 --dataset=<dataset_name>

Please move the trained model in the directory ./saved_model. Note that, we have already provided all the pre-trained models in the directory for quick re-evaluation.

Reproductivity

To reproduce the main results in Table 1 and Table 2, we have already provided all experiment logs in the directory ./logs/history. Run the following command in the directory ./scripts to reproduce the results in results.txt:

python show_result.py

3. Citation

If you find this repository helpful, please consider citing the following paper. We welcome any discussions with [email protected].

@inproceedings{yuan2023environmentaware,
  title={Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization},
  author={Yuan, Haonan and Sun, Qingyun and Fu, Xingcheng and Zhang, Ziwei and Ji, Cheng and Peng, Hao and Li, Jianxin},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
  year={2023},
  url={https://openreview.net/forum?id=n8JWIzYPRz}
}

4. Acknowledgements

Part of this code is inspired by Zeyang Zhang et al.'s DIDA. We owe sincere thanks to their valuable efforts and contributions.