Update README.md

README.md

@@ -11,8 +11,49 @@ datasets:
   format: csv
 ---
 
-Format:
+The dataset is dynamic graphs for paper [CrossLink](https://arxiv.org/pdf/2402.02168.pdf)
+
+<h5 align="center">
+<a href="https://arxiv.org/pdf/2402.02168.pdf"><img src="https://img.shields.io/badge/arXiv-2402.02168-b31b1b.svg" alt="arXiv"> </a>
+  <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>
+<a href="https://weichow23.github.io/CrossLink/"><img src="https://img.shields.io/badge/website-gold" alt="project website"> </a>
+<a href="https://huggingface.co/MeissonFlow/Meissonic">
+    <img src="https://img.shields.io/badge/🤗-Model-blue.svg"> </a>
+<a href="https://huggingface.co/datasets/WeiChow/DyGraphs">
+    <img src="https://img.shields.io/badge/🤗-Dataset-green.svg"> </a>
+</h5>
+
+## 🚀 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](./assets/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 raw data in [huggingface](https://huggingface.co/datasets/WeiChow/DyGraphs_raw)
+
+## 📚 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}, 
+}
+```