The CausalRanking repository contains code for a network diffusion based framework to identify significant causal anomalies and rank them. The method implemented here is described in this paper, and won the Best Paper Runner Up Award at SIGKDD'2016.
If you find the code in this respository useful for your research, please cite our paper:
@inproceedings{cheng2016ranking,
title={Ranking causal anomalies via temporal and dynamical analysis on vanishing correlations},
author={Cheng, Wei and Zhang, Kai and Chen, Haifeng and Jiang, Guofei and Chen, Zhengzhang and Wang, Wei},
booktitle={Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages={805--814},
year={2016}
}
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The baseline methods are also included, such as LBP and gRank, mRank.
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The code to calculate pair-wise correlations are included in ranking(sytheticdata).