DyTed: Disentangled Representation Learning for Discrete-time Dynamic Graph

This paper has been accepted by SIGKDD 2023. Link to the paper on ACM Digital Library

Authors

• Kaike Zhang
• Qi Cao
• Gaolin Fang
• Bingbing Xu
• Hongjian Zou
• Huawei Shen
• Xueqi Cheng

Abstract

Unsupervised representation learning for dynamic graphs has attracted a lot of research attention in recent years. Compared with static graph, the dynamic graph is a comprehensive embodiment of both the intrinsic stable characteristics of nodes and the time-related dynamic preference. However, existing methods generally mix these two types of information into a single representation space, which may lead to poor explanation, less robustness, and a limited ability when applied to different downstream tasks. To solve the above problems, in this paper, we propose a novel disenTangled representation learning framework for discrete-time Dynamic graphs, namely DyTed. We specially design a temporal-clips contrastive learning task together with a structure contrastive learning to effectively identify the time-invariant and time-varying representations respectively. To further enhance the disentanglement of these two types of representation, we propose a disentanglement-aware discriminator under an adversarial learning framework from the perspective of information theory. Extensive experiments on Tencent and five commonly used public datasets demonstrate that DyTed, as a general framework that can be applied to existing methods, achieves state-of-the-art performance on various downstream tasks, as well as be more robust against noise.

Environment

• networkx >=2.6.3
• numpy >= 1.22.2
• scikit-learn >= 1.0.2
• scipy >= 1.8.0
• torch >= 1.9.1
• torch-cluster >= 1.5.9
• torch-geometric >= 1.7.2
• torch-scatter >= 2.0.8
• torch-sparse >= 0.6.11
• torch-spline-conv >= 1.2.1

Usage (Quick Start)

1. Install the required packages using pip:

   pip install -r requirements.txt
   

2. Run the main script with the desired backbone model and dataset:

   python main.py --model=<backbone model> --dataset=<dataset>
   

   Replace <backbone model> with the name of your model, and <dataset> with the name of your dataset.

3. If you need to use more backbone models, make sure the model's input format is converted to a list compatible with torch_geometric.data. For converting sparse matrices to torch_geometric.data, refer to the _build_pyg_graphs() function inside the ./utilize/trainer.py file. The output should be a list of representations.

Citation

If you find our work useful, please cite our paper using the following BibTeX:

@inproceedings{zhang2023dyted,
  title={DyTed: Disentangled Representation Learning for Discrete-time Dynamic Graph},
  author={Zhang, Kaike and Cao, Qi and Fang, Gaolin and Xu, Bingbing and Zou, Hongjian and Shen, Huawei and Cheng, Xueqi},
  booktitle={Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages={3309--3320},
  year={2023}
}