ATD: Collaborative Research: Statistically Principled Real-Time Detection of Anomalies for Temporal Network Data

在我们日益相互联系的世界中，对由相互关联的实体组成的时间演变网络中的异常事件的检测正变得越来越重要。在这种情况下，异常检测的示例应用包括检测社交网络中的恐怖组织或仇恨团体，识别电网中负担过重的发电厂，以及发现金融市场中的非法活动。将这些问题转化为网络中的异常检测的一个主要好处是能够利用底层网络结构来显著提高检测能力。本研究旨在开发一种能保证良好检测性能的网络异常检测框架。本研究旨在开发一种两级流水线，用于静态和动态网络中的异常事件的统计原理检测。第一阶段使用网络的结构和时间演变来为网络上的每个节点生成持续演变的时间序列数据。这些多变量时间序列将建立在一系列特征的基础上，潜在地包括全局信息(例如来自谱嵌入的信息)和局部信息(例如节点参与子图模式，或所谓的“基元”)。因此，这项研究的一部分必然是开发有效的方法，随着网络的发展来计算和更新这些特征。第二阶段利用稳健统计学的最新发展，特别是多元分位数回归，整合辅助信息并标记潜在的异常情况，以供进一步研究。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The detection of anomalous events in time-evolving networks of interconnected entities is gaining importance in our increasingly connected world. Example applications of anomaly detection in this setting include detecting terrorist cells or hate groups in a social network, identifying over-burdened power plants in a power grid, and uncovering illegal activity in financial markets. A major benefit of casting these problems as anomaly detection in networks is the ability to leverage the underlying network structure to significantly improve detection power. This research aims to develop a framework for anomaly detection in networks that guarantees good detection performance.This research aims to develop a two-stage pipeline for statistically-principled detection of anomalous events in static and dynamic networks. The first stage uses the structure and temporal evolution of the network to generate continually evolving time-series data for each node on the network. These multivariate time-series will be built out of a range of features, potentially including global information such as that from the spectral embedding and local information such as a nodes participation in subgraph patterns, or so-called "motifs". Part of this research is therefore necessarily developing efficient means to compute and update these features as the network evolves. The second stage leverages recent developments in robust statistics, especially multivariate quantile regression, to integrate side information and flag potential anomalies for further investigation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.