Bayesian Discovery of Threat Networks

Abstract

A novel unified Bayesian framework for network detection is developed, under which a detection algorithm is derived based on random walks on graphs. The algorithm detects threat networks using partial observations of their activity, and is proved to be optimum in the Neyman-Pearson sense. The algorithm is defined by a graph, at least one observation, and a diffusion model for threat. A link to well-known spectral detection methods is provided, and the equivalence of the random walk and harmonic solutions to the Bayesian formulation is proven. A general diffusion model is introduced that utilizes spatio-temporal relationships between vertices, and is used for a specific space-time formulation that leads to significant performance improvements on coordinated covert networks. This performance is demonstrated using a new hybrid mixed-membership blockmodel introduced to simulate random covert networks with realistic properties.

IEEE Trans. Signal Process., major revision of arxiv.org/abs/1303.5613. arXiv admin note: substantial text overlap with arXiv:1303.5613

References in corpus (4)

• Finding community structure in networks using the eigenvectors of matrices
• Resolution limit in community detection
• Graph spectra and the detectability of community structure in networks
• Limited resolution in complex network community detection with Potts model approach

Cited by in corpus (7)

• Automatic Detection of Influential Actors in Disinformation Networks
• Streaming Graph Challenge: Stochastic Block Partition
• A Spectral Framework for Anomalous Subgraph Detection
• Contagion Source Detection in Epidemic and Infodemic Outbreaks: Mathematical Analysis and Network Algorithms
• Influence Estimation on Social Media Networks Using Causal Inference
• From random walks to distances on unweighted graphs
• The Power of Side-information in Subgraph Detection