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Hierarchical Dense Subtensor Detection in Tensors

CatchCore is a novel framework to detect hierarchical dense cores in multi-aspect data (i.e. tensors). CatchCore has the following properties:

• unified metric: provides a gradient-based optimized framework as well as theoretical guarantees
• accurate: provides high accuracy in both synthetic and real data
• effectiveness: spots anomaly patterns and hierarchical dense community
• scalable: scales almost linearly with all factors of input tensor, also has linearly space complexity

Datasets

The download links for the datasets used in the paper are available online.

• Android App rating. 1.32M × 61.3K × 1.28K × 5
• BeerAdvocate rating. 26.5K × 50.8K × 1472 × 1
• StackOverflow favorite. 545K × 96.7K × 1.15K × 1
• DBLP Co-author. 1.31M × 1.31M × 72
• Youtube Favorite. 3.22M × 3.22M × 203
• DARPA TCP Dumps. 9.48K × 23.4K × 46.6K
• AirForce TCP Dumps. 3 × 70 × 11 × 7.20K × 21.5K × 512 × 512

Environment

To install required libraries, please type

pip install -r requirements

Building and Running CatchCore

Please see User Guide

Running Demo

Demo for detecting hierarchical dense subtensor, please type

make

Reference

If you use this code as part of any published research, please acknowledge the following papers.

@article{feng2023hierarchical,
  title={Hierarchical Dense Pattern Detection in Tensors},
  author={Feng, Wenjie and Liu, Shenghua and Cheng, Xueqi},
  journal={ACM Transactions on Knowledge Discovery from Data},
  volume={17},
  number={6},
  pages={1--29},
  year={2023},
  publisher={ACM New York, NY}
}

@inproceedings{feng2019catchcore,
  title={CatchCore: Catching Hierarchical Dense Subtensor},
  author={Wenjie Feng, Shenghua Liu, and Xueqi Cheng},
  booktitle={European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)},
  year={2019},
}