DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams

Kijung Shin, Bryan Hooi, Jisu Kim, Christos Faloutsos

Abstract: Consider a stream of retweet events - how can we spot fraudulent lock-step behavior in such multi-aspect data (i.e., tensors) evolving over time? Can we detect it in real time, with an accuracy guarantee? Past studies have shown that dense subtensors tend to indicate anomalous or even fraudulent behavior in many tensor data, including social media, Wikipedia, and TCP dumps. Thus, several algorithms have been proposed for detecting dense subtensors rapidly and accurately. However, existing algorithms assume that tensors are static, while many real-world tensors, including those mentioned above, evolve over time. We propose DENSESTREAM, an incremental algorithm that maintains and updates a dense subtensor in a tensor stream (i.e., a sequence of changes in a tensor), and DENSESALERT, an incremental algorithm spotting the sudden appearances of dense subtensors. Our algorithms are: (1) Fast and "any time": updates by our algorithms are up to a million times faster than the fastest batch algorithms, (2) Provably accurate: our algorithms guarantee a lower bound on the density of the subtensor they maintain, and (3) Effective: our DENSESALERT successfully spots anomalies in real-world tensors, especially those overlooked by existing algorithms.