This is the source code for paper Learning Dynamic Context Graphs for Predicting Social Events appeared in KDD2019 (research track)
Songgaojun Deng, Huzefa Rangwala, Yue Ning
- ICEWS event data is available online.
- ICEWS news data has not been released publicly.
- PyTorch >= 1.0
- sklearn
- pytorch_sparse Refer to the official website to install.
- THAD6h (Thailand dynamic (temporal) dataset, around 600 nodes per graph) Link
- INDD6h Link
- EGYD6h Link
- RUSD6h Link
- *.idx / *.tidx Word index file for training/testing
- *.x / *.tx Temporal graph input file for training/testing
- *.y / *.ty Ground truth for training/testing
Please cite our paper if you find this code useful for your research:
@inproceedings{deng2019learning,
title={Learning Dynamic Context Graphs for Predicting Social Events},
author={Deng, Songgaojun and Rangwala, Huzefa and Ning, Yue},
booktitle={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
pages={1007--1016},
year={2019}
}