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 real-world tensors.
are described in the following paper:
DenseAlert: Incremental Dense-Subtensor 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]
The source code used in the paper is available. [Github Repository]