@inproceedings{d146b39e08eb4dcb9a3e8f891f935bb5,
title = "Detecting Anomalies in Time-Varying Networks Using Tensor Decomposition",
abstract = "New data sources from sensor networks and Internet-of-Things applications promise a wealth of interaction data that can be naturally represented as time-varying networks. This brings forth new challenges for the identificationand removal of time-varying graph anomalies that entail complex correlations of topological features and temporal activity patterns. Here we present an anomaly detection approach for temporal graph data, based on an iterative tensor decomposition and masking procedure. We test this approach using high-resolution social network data from wearable proximity sensors. The dataset includes metadata that allow to independently build a ground truth, used to validate the anomaly detection method. Our approach achieves high accuracy in identifying meso-scale network anomalies due to sensor wearing protocol, proving the practical viability of the method for a real-world application.",
author = "Anna Sapienza and Andre Panisson and Joseph Wu and Laetitia Gauvin and Ciro Cattuto",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 ; Conference date: 14-11-2015 Through 17-11-2015",
year = "2016",
month = jan,
day = "29",
doi = "10.1109/ICDMW.2015.128",
language = "English",
series = "Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "516--523",
editor = "Xindong Wu and Alexander Tuzhilin and Hui Xiong and Dy, \{Jennifer G.\} and Charu Aggarwal and Zhi-Hua Zhou and Peng Cui",
booktitle = "Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015",
address = "United States",
}