yinyiusc/dcube

D-Cube: Dense-Block Detection in Terabyte-Scale Tensors

D-Cube: Dense-Block Detection in Terabyte-Scale Tensors

D-Cube (Disk-based Dense-block Detection) is an algorithm for detecting dense subtensors in tensors. D-Cube has the following properties:

• scalable: D-Cube handles large data not fitting in memory or even on a disk.
• fast: Even when data fit in memory, D-Cube outperforms its competitors in terms of speed.
• accurate: D-Cube gives high accuracy in real-world data as well as theoretical accuracy guarantees.

Datasets

The download links for the datasets used in the paper are here

Building and Running D-Cube

Please see User Guide

Running Demo

For demo, please type 'make'

Reference

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

@inproceedings{shin2017disk,
  author    = {Kijung Shin and Bryan Hooi and Jisu Kim and Christos Faloutsos},
  title     = {D-Cube: Dense-Block Detection in Terabyte-Scale Tensors},
  booktitle={Proceedings of the Ninth ACM International Conference on Web Search and Data Mining},
  year={2017},
  organization={ACM}
}