Network Inference From Consensus Dynamics With Unknown Parameters
Name
1908.01393.pdf
Description
Accepted version
Size
1.03 MB
Format
Adobe PDF
Checksum (MD5)
b47e6c59d8b462f69b45989071a84f22
Author(s)
Zhu, Yu
Schaub, Michael T
Jadbabaie, Ali
Segarra, Santiago
Date Issued
2020
Journal
IEEE Transactions on Signal and Information Processing over Networks
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Zhu, Yu, Schaub, Michael T, Jadbabaie, Ali and Segarra, Santiago. 2020. "Network Inference From Consensus Dynamics With Unknown Parameters." IEEE Transactions on Signal and Information Processing over Networks, 6.
Version
Author's final manuscript
Abstract
© 2015 IEEE. We 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. Specifically, we consider three problems in which we assume different levels of knowledge about the diffusion rates, observation times, and the input signal power of the dynamics. To solve these underdetermined problems, we propose a set of algorithms that leverage the spectral properties of the observed data and tools from convex optimization. Furthermore, we provide theoretical performance guarantees associated with these algorithms. We complement our theoretical work with numerical experiments, that demonstrate how our proposed methods outperform current state-of-the-art algorithms and showcase their effectiveness in recovering both synthetic and real-world networks.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/TSIPN.2020.2984499
