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.
2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)