Naslov
Incorporating Latent Constraints to Enhance Inference of Network Structure

Autori
Huang, Keke ; Wang, Zhen ; Jusup, Marko

Izvornik
IEEE Transactions on Network Science and Engineering (2327-4697) 7 (2020), 1; 466-475

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Complex network ; compressive sensing ; latent structural constraint ; network structure identification

Sažetak
A complex network is a model representation of interactions within technological, social, information, and biological networks. Oftentimes, we are interested in identifying the underlying network structure from limited and noisy observational data, which is a challenging problem. Here, to address this problem, we propose a novel and effective technique that incorporates latent structural constraints into binary compressed sensing. We show high accuracy and robust effectiveness of our proposed method by analyzing artificial small-world and scale-free networks, as well as two empirical networks. Our method requires a relatively small number of observations and it is robust against strong measurement noise. These results suggest that incorporating latent structural constraints into an algorithm for identifying the underlying network structure improves the inference of connections in complex networks.

Izvorni jezik
Engleski

Znanstvena područja
Interdisciplinarne prirodne znanosti, Interdisciplinarne tehničke znanosti

POVEZANOST RADA