---
license: apache-2.0
tags:
  - text
  - graph
task_categories:
  - graph-ml
language:
  - en
datasets:
  format: csv
---


---
license: apache-2.0
tags:
  - text
  - graph
task_categories:
  - graph-ml
language:
  - en
datasets:
  format: csv
---

The dataset is dynamic graphs for paper [CrossLink](https://arxiv.org/pdf/2402.02168.pdf). The usage of this dataset can be seen in [Github](https://weichow23.github.io/CrossLink/)

## 🚀 Introduction

CrossLink learns the evolution pattern of a specific downstream graph and subsequently makes pattern-specific link predictions. 
It employs a technique called *conditioned link generation*, which integrates both evolution and structure modeling to perform evolution-specific link prediction. This conditioned link generation is carried out by a transformer-decoder architecture, enabling efficient parallel training and inference. CrossLink is trained on extensive dynamic graphs across diverse domains, encompassing 6 million dynamic edges. Extensive experiments on eight untrained graphs demonstrate that CrossLink achieves state-of-the-art performance in cross-domain link prediction.  Compared to advanced baselines under the same settings, CrossLink shows an average improvement of **11.40%** in Average Precision across eight graphs. Impressively, it surpasses the fully supervised performance of 8 advanced baselines on 6 untrained graphs.

![Architecture](model.png)

## Format

Please keep the dataset in the fellow format:

| Unnamed: 0 | u             | i             | ts                 | label        | idx                    |
| ---------- | ------------- | ------------- | ------------------ | ------------ | ---------------------- |
| `idx-1`    | `source node` | `target node` | `interaction time` | `defalut: 0` | `from 1 to the #edges` |

You can prepare those data by the code in `preprocess_data` folder

You can also use our processed data in [huggingface](https://huggingface.co/datasets/WeiChow/DyGraphs)

## 📚 Citation

If you find this work helpful, please consider citing:

```bibtex
@misc{huang2024graphmodelcrossdomaindynamic,
  title={One Graph Model for Cross-domain Dynamic Link Prediction}, 
  author={Xuanwen Huang and Wei Chow and Yang Wang and Ziwei Chai and Chunping Wang and Lei Chen and Yang Yang},
  year={2024},
  eprint={2402.02168},
  archivePrefix={arXiv},
  primaryClass={cs.LG},
  url={https://arxiv.org/abs/2402.02168}, 
}
```