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Grant support

Work in this paper was supported by the Spanish MINECO grants Klinilycs (TEC2016-75361-R), Instituto de Salud Carlos III DTS17/00158 and FPU17/04520. All authors are with the Dept. of Signal Theory and Comms., King Juan Carlos University, Madrid, Spain.

Analysis of institutional authors

Buciulea, AAuthorRey, SAuthorMarques, AgCorresponding Author

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Proceedings Paper

Network Reconstruction from Graph-stationary Signals with Hidden Variables

Publicated to:Conference Record Of The Asilomar Conference On Signals, Systems And Computers. 2019-November 56-60 - 2019-01-01 2019-November(), DOI: 10.1109/IEEECONF44664.2019.9048913

Authors: Buciulea, Andrei; Rey, Samuel; Cabrera, Cristobal; Marques, Antonio G

Affiliations

King Juan Carlos Univ, Dept Signal Theory & Comms, Madrid, Spain - Author

Abstract

Network topology inference from nodal observations has attracted a lot of attention in different fields with a wide variety of applications. While most of the existing works assume that signal observations at all the nodes are available, this is not always the case. We investigate the problem of inferring the topology of an undirected network in the presence of hidden variables, meaning that only a subset of the nodes of the graph is being observed. To address this problem it is assumed that: (i) the nodal signals are stationary in the unknown underlying graph; and (ii) the number of observed nodes is considerably larger than the number of hidden variables. Graph stationarity implies that the covariance of the nodal signals can be expressed as a polynomial of the matrix representation of the whole graph. Rooted in this prior knowledge, the network topology inference is approached as a (low-rank and sparse) optimization problem where we aim to recover the matrix representation of the observed nodes without ignoring the influence of the hidden variables. Different convex relaxations are proposed and robust designs are presented. Finally, numerical experiments using simulations showcase the performance of the developed methods and compare them with existing alternatives.

Keywords

Computer circuitsConvex relaxationCovariance matrixGraph learningGraph signal processingGraph theoryHidden nodesLatent variablesMatrix representationNetwork reconstructionNetwork topology inferenceNumerical experimentsNumerical methodsOptimization problemsRelaxation processesSelectionSignal reconstructionStationary signalUnderlying graphsUndirected network

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Conference Record Of The Asilomar Conference On Signals, Systems And Computers due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2019, it was in position , thus managing to position itself as a Q2 (Segundo Cuartil), in the category Computer Networks and Communications.

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 2.42, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Jun 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-06-18, the following number of citations:

  • WoS: 9
  • Scopus: 10
  • OpenCitations: 7

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-18:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 1 (PlumX).

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Buciulea Vlas, Andrei) and Last Author (García Marqués, Antonio).

the author responsible for correspondence tasks has been García Marqués, Antonio.