Abstract
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.
Original language | English (US) |
---|---|
Title of host publication | 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 375-379 |
Number of pages | 5 |
ISBN (Electronic) | 9781509045457 |
DOIs | |
State | Published - Apr 19 2017 |
Event | 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States Duration: Dec 7 2016 → Dec 9 2016 |
Publication series
Name | 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings |
---|
Other
Other | 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 |
---|---|
Country/Territory | United States |
City | Washington |
Period | 12/7/16 → 12/9/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.
Keywords
- Dynamic networks
- Network inference
- Structural equation models (SEMs)
- Tensor decomposition