Overview
DenseStream is an incremental algorithm for detecting dense subtensors in tensor streams, and
DenseAlert is an incremental algorithm for spotting suddenly emerging dense subtensors. They have the following properties:

Fast and Any Time: By maintaining and updating a dense subtensor, our algorithms detect a dense subtensor in a tensor stream significantly faster than batch algorithms.

Provably Accurate: Our algorithms provide theoretical guarantees on their accuracy, and show high accuracy in practice.

Effective: Our algorithms successfully identify anomalies, such as bot activities, rating manipulations, and network intrusions, in realworld tensors.
Paper
DenseStream and
DenseAlert are described in the following paper:

DenseAlert: Incremental DenseSubtensor Detection in Tensor Streams
Kijung Shin, Bryan Hooi, Jisu Kim, and Christos Faloutsos
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2017, Halifax, Canada
[PDF] [Supplementary Document] [BIBTEX]
Codes
The source code used in the paper is available.
[Github Repository]
Datasets
People