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---
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.
## 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},
}
```
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