Anomaly detection in dynamic networks: a survey

Abstract

Anomaly detection is an important problem with multiple applications, and thus has been studied for decades in various research domains. In the past decade there has been a growing interest in anomaly detection in data represented as networks, or graphs, largely because of their robust expressiveness and their natural ability to represent complex relationships. Originally, techniques focused on anomaly detection in static graphs, which do not change and are capable of representing only a single snapshot of data. As real‐world networks are constantly changing, there has been a shift in focus to dynamic graphs, which evolve over time. In this survey, we aim to provide a comprehensive overview of anomaly detection in dynamic networks, concentrating on the state‐of‐the‐art methods. We first describe four types of anomalies that arise in dynamic networks, providing an intuitive explanation, applications, and a concrete example for each. Having established an idea for what constitutes an anomaly, a general two‐stage approach to anomaly detection in dynamic networks that is common among the methods is presented. We then construct a two‐tiered taxonomy, first partitioning the methods based on the intuition behind their approach, and subsequently subdividing them based on the types of anomalies they detect. Within each of the tier one categories—community, compression, decomposition, distance, and probabilistic model based—we highlight the major similarities and differences, showing the wealth of techniques derived from similar conceptual approaches.WIREs Comput Stat 2015, 7:223–247. doi: 10.1002/wics.1347 This article is categorized under:Algorithms and Computational Methods > Algorithms Data: Types and Structure > Graph and Network Data Statistical Learning and Exploratory Methods of the Data Sciences > Pattern Recognition