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Yu Zhu
Researcher at Rice University
Publications - 11
Citations - 128
Yu Zhu is an academic researcher from Rice University. The author has contributed to research in topics: Laplacian matrix & Signal processing. The author has an hindex of 4, co-authored 11 publications receiving 53 citations. Previous affiliations of Yu Zhu include Beihang University.
Papers
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Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyond
TL;DR: In this article, the authors provide a didactic treatment of the emerging topic of signal processing on higher-order networks, with a special emphasis on the concepts needed for the processing of signals supported on these structures.
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Network Inference From Consensus Dynamics With Unknown Parameters
TL;DR: In this paper, the authors explore the problem of inferring the graph Laplacian of a weighted, undirected network from snapshots of a single or multiple discrete-time consensus dynamics, subject to parameter uncertainty, taking place on the network.
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Estimating Network Processes via Blind Identification of Multiple Graph Filters
TL;DR: In this paper, the authors consider the problem of jointly estimating multiple network processes driven by a common unknown input, thus effectively generalizing the classical blind multi-channel identification problem to graphs.
Journal ArticleDOI
Network Inference from Consensus Dynamics with Unknown Parameters
TL;DR: This work proposes a set of algorithms that leverage the spectral properties of the observed data and tools from convex optimization to solve underdetermined problems of inferring the graph Laplacian of a weighted, undirected network from snapshots of a single or multiple discrete-time consensus dynamics.
Posted Content
Estimating Network Processes via Blind Identification of Multiple Graph Filters
TL;DR: This paper studies the problem of jointly estimating multiple network processes driven by a common unknown input, thus effectively generalizing the classical blind multi-channel identification problem to graphs and proposes a sparse recovery algorithm with theoretical performance guarantees.