Applying a passive network reconstruction technique to Twitter data in order to identify trend setters

Chetty, Vasu; Woodbury, Nathan; Brewer, Jacob; Lee, Kenneth; Warnick, Sean

doi:10.1109/ccta.2017.8062645

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Importantly, we consider situations where only a subset of the output nodes in y is being measured. This will play a fundamental role to suitably apply our results to situations where the inputs are non-target specific [3], for example when dealing with confounding variables, by assuming that an input is an unmeasured node.…”

Section: Problem Formulations: Irrelevance Of Measurements and Identi...mentioning

confidence: 99%

Signal Selection for Estimation and Identification in Networks of Dynamic Systems: A Graphical Model Approach

Materassi

Salapaka

2020

IEEE Trans. Automat. Contr.

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Network systems have become a ubiquitous modeling tool in many areas of science where nodes in a graph represent distributed processes and edges between nodes represent a form of dynamic coupling. When a network topology is already known (or partially known), two associated goals are (i) to derive estimators for nodes of the network which cannot be directly observed or are impractical to measure; and (ii) to quantitatively identify the dynamic relations between nodes. In this article we address both problems in the challenging scenario where only some outputs of the network are being measured and the inputs are not accessible. The approach makes use of the notion of d-separation for the graph associated with the network. In the considered class of networks, it is shown that the proposed technique can determine or guide the choice of optimal sparse estimators. The article also derives identification methods that are applicable to cases where loops are present providing a different perspective on the problem of closed-loop identification. The notion of d-separation is a central concept in the area of probabilistic graphical models, thus an additional contribution is to create connections between control theory and machine learning techniques.

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Section: Problem Formulations: Irrelevance Of Measurements and Identi...mentioning

confidence: 99%

Signal Selection for Estimation and Identification in Networks of Dynamic Systems: A Graphical Model Approach

Materassi

Salapaka

2020

IEEE Trans. Automat. Contr.

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“…System structures can also be described in terms of its subsystem nature with the modeling technique called linear fractional transformation. In comparison, modeling technique we called dynamical structure function [26] is one that concerns with the signal structure This problem has been formulated in [11], and it has been applied to biochemical reaction networks, financial networks [25], and social networks [7].…”

Section: Representations Of System Structuresmentioning

confidence: 99%

Necessary and Sufficient Conditions on State Transformations That Preserve the Causal Structure of LTI Dynamical Networks

Leung

Warnick

2018

2018 IEEE Conference on Decision and Control (CDC)

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Connecting graphical notions of separation and statistical notions of independence for topology reconstruction

Materassi

2023

2023 62nd IEEE Conference on Decision and Control (CDC)

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Applying a passive network reconstruction technique to Twitter data in order to identify trend setters

Cited by 3 publications

References 17 publications

Signal Selection for Estimation and Identification in Networks of Dynamic Systems: A Graphical Model Approach

Signal Selection for Estimation and Identification in Networks of Dynamic Systems: A Graphical Model Approach

Necessary and Sufficient Conditions on State Transformations That Preserve the Causal Structure of LTI Dynamical Networks

Connecting graphical notions of separation and statistical notions of independence for topology reconstruction

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