TY - GEN
T1 - Dynamic structural equation models for tracking topologies of social networksy
AU - Baingana, Brian
AU - Mateos, Gonzalo
AU - Giannakis, Georgios B.
PY - 2013
Y1 - 2013
N2 - Many real-world processes evolve in cascades over complex networks, whose topologies are often unobservable and change over time. However, the so-termed adoption times when blogs mention popular news items, individuals in a community catch an infectious disease, or consumers adopt a trendy electronics product are typically known, and are implicitly dependent on the underlying network. To infer the network topology, a dynamic structural equation model is adopted that captures the relationship between observed adoption times and the unknown edge weights. Assuming a slowly time-varying network and leveraging the sparse connectivity inherent to social networks, edge weights are estimated by minimizing a sparsity-regularized exponentially-weighted least-squares criterion. An alternating-direction method of multipliers solver is developed to this end, and preliminary tests on synthetic network data corroborate the effectiveness of the novel algorithm in unveiling the dynamically-evolving network topology.
AB - Many real-world processes evolve in cascades over complex networks, whose topologies are often unobservable and change over time. However, the so-termed adoption times when blogs mention popular news items, individuals in a community catch an infectious disease, or consumers adopt a trendy electronics product are typically known, and are implicitly dependent on the underlying network. To infer the network topology, a dynamic structural equation model is adopted that captures the relationship between observed adoption times and the unknown edge weights. Assuming a slowly time-varying network and leveraging the sparse connectivity inherent to social networks, edge weights are estimated by minimizing a sparsity-regularized exponentially-weighted least-squares criterion. An alternating-direction method of multipliers solver is developed to this end, and preliminary tests on synthetic network data corroborate the effectiveness of the novel algorithm in unveiling the dynamically-evolving network topology.
UR - http://www.scopus.com/inward/record.url?scp=84894200871&partnerID=8YFLogxK
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U2 - 10.1109/CAMSAP.2013.6714065
DO - 10.1109/CAMSAP.2013.6714065
M3 - Conference contribution
AN - SCOPUS:84894200871
SN - 9781467331463
T3 - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
SP - 292
EP - 295
BT - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
T2 - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
Y2 - 15 December 2013 through 18 December 2013
ER -