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. The usage of this dataset can be seen in Github
🚀 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
📚 Citation
If you find this work helpful, please consider citing:
@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},
}