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--- |
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license: apache-2.0 |
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tags: |
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- text |
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- graph |
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task_categories: |
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- graph-ml |
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language: |
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- en |
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datasets: |
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format: csv |
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--- |
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--- |
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license: apache-2.0 |
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tags: |
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- text |
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- graph |
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task_categories: |
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- graph-ml |
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language: |
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- en |
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datasets: |
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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). The usage of this dataset can be seen in [Github](https://weichow23.github.io/CrossLink/) |
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## 🚀 Introduction |
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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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## Format |
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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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You can prepare those data by the code in `preprocess_data` folder |
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You can also use our processed data in [huggingface](https://huggingface.co/datasets/WeiChow/DyGraphs) |
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## 📚 Citation |
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If you find this work helpful, please consider citing: |
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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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``` |
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