Anomaly Detection in Large-Scale Networks With Latent Space Models.

ABSTRACT: We develop a real-time anomaly detection method for directed activity on large, sparse networks. We model the propensity for future activity using a dynamic logistic model with interaction terms for sender- and receiver-specific latent factors in addition to sender- and receiver-specific popularity scores; deviations from this underlying model constitute potential anomalies. Latent nodal attributes are estimated via a variational Bayesian approach and may change over time, representing natural shifts in network activity. Estimation is augmented with a case-control approximation to take advantage of the sparsity of the network and reduces computational complexity from O(N ²) to O(E), where N is the number of nodes and E is the number of o

SUBMITTER: Lee W 

PROVIDER: S-EPMC11534301 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature