Tracking dynamic piecewise-constant network topologies via adaptive tensor factorization

This paper deals with tracking dynamic piecewise-constant network topologies that underpin complex systems including online social networks, neural pathways in the brain, and the world-wide web. Leveraging a structural equation model (SEM) in which only second-order statistics of exogenous inputs are known, the topology inference problem is recast using three-way tensors constructed from observed nodal data. To facilitate real-time operation, an adaptive parallel factor (PARAFAC) tensor decomposition is advocated to track the topology-revealing tensor factors. Preliminary tests on simulated data corroborate the effectiveness of the novel tensor-based approach.

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