Hypergraph-Based Anomaly Detection of High-Dimensional Co-Occurrences
This paper addresses the problem of detecting anomalous multivariate co-occurrences using a limited number of unlabeled training observations. A novel method based on using a hypergraph representation of the data is proposed to deal with this very high-dimensional problem. Hypergraphs constitute an important extension of graphs that allow edges to connect more than two vertices simultaneously. A variational Expectation-Maximization algorithm for detecting anomalies directly on the hypergraph domain without any feature selection or dimensionality reduction is presented. The resulting estimate can be used to calculate a measure of anomalousness based on the false-discovery rate. The algorithm has O(np) computational complexity, where n is the number of training observations and p is the number of potential participants in each co-occurrence event. This efficiency makes the method ideally suited for very high-dimensional settings and requires no tuning, bandwidth, or regularization parameters. The proposed approach is validated on both high-dimensional synthetic data and the Enron e-mail database, where p > 75,000, and it is shown that it can outperform other state-of-the-art methods.
anomaly detection co-occurrence analysis unsupervised learning variational methods social networks