no code implementations • 26 Oct 2016 • Yanning Shen, Brian Baingana, Georgios B. Giannakis
The present paper advocates a novel SEM-based topology inference approach that entails factorization of a three-way tensor, constructed from the observed nodal data, using the well-known parallel factor (PARAFAC) decomposition.
no code implementations • 20 Oct 2016 • Yanning Shen, Brian Baingana, Georgios B. Giannakis
To unify these complementary perspectives, linear structural vector autoregressive models (SVARMs) that leverage both contemporaneous and time-lagged nodal data have recently been put forth.
no code implementations • 28 Jun 2016 • Brian Baingana, Georgios B. Giannakis
Contagions such as the spread of popular news stories, or infectious diseases, propagate in cascades over dynamic networks with unobservable topologies.
no code implementations • 10 May 2016 • Yanning Shen, Brian Baingana, Georgios B. Giannakis
Interestingly, pursuit of the novel kernel-based approach yields a convex regularized estimator that promotes edge sparsity, and is amenable to proximal-splitting optimization methods.
no code implementations • 25 Jun 2015 • Brian Baingana, Georgios B. Giannakis
Efficient tracking algorithms suitable for both online and decentralized operation are developed.
no code implementations • 17 Jan 2014 • Brian Baingana, Georgios B. Giannakis
Visual rendering of graphs is a key task in the mapping of complex network data.