TY - GEN
T1 - A proximal gradient algorithm for tracking cascades over networks
AU - Baingana, Brian
AU - Mateos, Gonzalo
AU - Giannakis, Georgios B.
PY - 2014
Y1 - 2014
N2 - Many real-world processes evolve in cascades over networks, whose topologies are often unobservable and change over time. However, the so-termed adoption times when for instance blogs mention popular news items are typically known, and are implicitly dependent on the underlying network. To infer the network topology, a dynamic structural equation model is adopted to capture the relationship between observed adoption times and the unknown edge weights, while accounting also for external (non-topological) perturbations. Assuming a slowly time-varying topology and leveraging the sparse connectivity inherent to social networks, edge weights are estimated by minimizing a sparsity-regularized exponentially-weighted least-squares criterion. To this end, a solver is developed by leveraging (pseudo) real-time sparsity-promoting proximal gradient iterations. Numerical tests with real cascades of online media demonstrate the effectiveness of the novel algorithm in unveiling sparse dynamically-evolving topologies.
AB - Many real-world processes evolve in cascades over networks, whose topologies are often unobservable and change over time. However, the so-termed adoption times when for instance blogs mention popular news items are typically known, and are implicitly dependent on the underlying network. To infer the network topology, a dynamic structural equation model is adopted to capture the relationship between observed adoption times and the unknown edge weights, while accounting also for external (non-topological) perturbations. Assuming a slowly time-varying topology and leveraging the sparse connectivity inherent to social networks, edge weights are estimated by minimizing a sparsity-regularized exponentially-weighted least-squares criterion. To this end, a solver is developed by leveraging (pseudo) real-time sparsity-promoting proximal gradient iterations. Numerical tests with real cascades of online media demonstrate the effectiveness of the novel algorithm in unveiling sparse dynamically-evolving topologies.
KW - Social network
KW - cascade
KW - convex optimization
KW - structural equation model
KW - topology inference
UR - http://www.scopus.com/inward/record.url?scp=84905284084&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84905284084&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2014.6854509
DO - 10.1109/ICASSP.2014.6854509
M3 - Conference contribution
AN - SCOPUS:84905284084
SN - 9781479928927
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 4778
EP - 4782
BT - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
Y2 - 4 May 2014 through 9 May 2014
ER -