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Joint Network Topology Inference in the Presence of Hidden Nodes

dc.contributor.authorNavarro, Madeline
dc.contributor.authorRey, Samuel
dc.contributor.authorBuciulea, Andrei
dc.contributor.authorGarcia Marques, Antonio
dc.contributor.authorSegarra, Santiago
dc.identifier.citationM. Navarro, S. Rey, A. Buciulea, A. G. Marques and S. Segarra, "Joint Network Topology Inference in the Presence of Hidden Nodes," in IEEE Transactions on Signal Processing, doi: 10.1109/TSP.2024.3392348es
dc.identifier.issn1941-0476 (online)
dc.identifier.issn1053-587X (print)
dc.description.abstractWe investigate the increasingly prominent task of jointly inferring multiple networks from nodal observations. While most joint inference methods assume that observations are available at all nodes, we consider the realistic and more difficult scenario where a subset of nodes are hidden and cannot be measured. Under the assumptions that the partially observed nodal signals are graph stationary and the networks have similar connectivity patterns, we derive structural characteristics of the connectivity between hidden and observed nodes. This allows us to formulate an optimization problem for estimating networks while accounting for the influence of hidden nodes. We identify conditions under which a convex relaxation yields the sparsest solution, and we formalize the performance of our proposed optimization problem with respect to the effect of the hidden nodes. Finally, synthetic and real-world simulations provide evaluations of our method in comparison with other baselineses
dc.publisherInstitute of Electrical and Electronics Engineerses
dc.titleJoint Network Topology Inference in the Presence of Hidden Nodeses

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