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@@ -11,8 +11,49 @@ datasets:
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  format: csv
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  ---
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- Format:
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  | Unnamed: 0 | u | i | ts | label | idx |
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  | ---------- | ------------- | ------------- | ------------------ | ------------ | ---------------------- |
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  | `idx-1` | `source node` | `target node` | `interaction time` | `defalut: 0` | `from 1 to the #edges` |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  format: csv
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  ---
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+ The dataset is dynamic graphs for paper [CrossLink](https://arxiv.org/pdf/2402.02168.pdf)
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+
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+ <h5 align="center">
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+ <a href="https://arxiv.org/pdf/2402.02168.pdf"><img src="https://img.shields.io/badge/arXiv-2402.02168-b31b1b.svg" alt="arXiv"> </a>
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+ <a href="https://github.com/weichow23/CrossLink"><img src="https://img.shields.io/github/stars/weichow23/CrossLink?style=social&amp;logo=github" width="75pt"></a>
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+ <a href="https://weichow23.github.io/CrossLink/"><img src="https://img.shields.io/badge/website-gold" alt="project website"> </a>
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+ <a href="https://huggingface.co/MeissonFlow/Meissonic">
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+ <img src="https://img.shields.io/badge/🤗-Model-blue.svg"> </a>
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+ <a href="https://huggingface.co/datasets/WeiChow/DyGraphs">
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+ <img src="https://img.shields.io/badge/🤗-Dataset-green.svg"> </a>
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+ </h5>
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+
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+ ## 🚀 Introduction
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+
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+ CrossLink learns the evolution pattern of a specific downstream graph and subsequently makes pattern-specific link predictions.
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+ 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.
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+
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+ ![Architecture](./assets/model.png)
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+
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+ #### Format
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+
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+ Please keep the dataset in the fellow format:
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  | Unnamed: 0 | u | i | ts | label | idx |
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  | ---------- | ------------- | ------------- | ------------------ | ------------ | ---------------------- |
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  | `idx-1` | `source node` | `target node` | `interaction time` | `defalut: 0` | `from 1 to the #edges` |
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+
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+ You can prepare those data by the code in `preprocess_data` folder
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+
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+ You can also use our raw data in [huggingface](https://huggingface.co/datasets/WeiChow/DyGraphs_raw)
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+
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+ ## 📚 Citation
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+
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+ If you find this work helpful, please consider citing:
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+
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+ ```bibtex
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+ @misc{huang2024graphmodelcrossdomaindynamic,
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+ title={One Graph Model for Cross-domain Dynamic Link Prediction},
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+ author={Xuanwen Huang and Wei Chow and Yang Wang and Ziwei Chai and Chunping Wang and Lei Chen and Yang Yang},
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+ year={2024},
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+ eprint={2402.02168},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.LG},
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+ url={https://arxiv.org/abs/2402.02168},
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+ }
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+ ```