Network Inference From Consensus Dynamics With Unknown Parameters
© 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 d...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2023
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| Online Access: | https://hdl.handle.net/1721.1/148599 |
| _version_ | 1826213966929461248 |
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| author | Zhu, Yu Schaub, Michael T Jadbabaie, Ali Segarra, Santiago |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Zhu, Yu Schaub, Michael T Jadbabaie, Ali Segarra, Santiago |
| author_sort | Zhu, Yu |
| collection | MIT |
| description | © 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. |
| first_indexed | 2024-09-23T15:57:41Z |
| format | Article |
| id | mit-1721.1/148599 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:57:41Z |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1485992023-03-18T03:04:46Z Network Inference From Consensus Dynamics With Unknown Parameters Zhu, Yu Schaub, Michael T Jadbabaie, Ali Segarra, Santiago Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Massachusetts Institute of Technology. Institute for Data, Systems, and Society © 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. 2023-03-17T16:22:38Z 2023-03-17T16:22:38Z 2020 2023-03-17T16:12:47Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/148599 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. en 10.1109/TSIPN.2020.2984499 IEEE Transactions on Signal and Information Processing over Networks Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) arXiv |
| spellingShingle | Zhu, Yu Schaub, Michael T Jadbabaie, Ali Segarra, Santiago Network Inference From Consensus Dynamics With Unknown Parameters |
| title | Network Inference From Consensus Dynamics With Unknown Parameters |
| title_full | Network Inference From Consensus Dynamics With Unknown Parameters |
| title_fullStr | Network Inference From Consensus Dynamics With Unknown Parameters |
| title_full_unstemmed | Network Inference From Consensus Dynamics With Unknown Parameters |
| title_short | Network Inference From Consensus Dynamics With Unknown Parameters |
| title_sort | network inference from consensus dynamics with unknown parameters |
| url | https://hdl.handle.net/1721.1/148599 |
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