Bayesian topology learning and noise removal from network data
Learning the topology of a graph from available data is of great interest in many emerging applications. Some examples are social networks, internet of things networks (intelligent IoT and industrial IoT), biological connection networks, sensor networks and traffic network patterns. In this paper, a...
| Main Authors: | , , , , , |
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| Format: | Journal article |
| Language: | English |
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Springer
2021
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| _version_ | 1826312548608114688 |
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| author | Ramezani Mayiami, M Hajimirsadeghi, M Skretting, K Dong, X Blum, RS Poor, HV |
| author_facet | Ramezani Mayiami, M Hajimirsadeghi, M Skretting, K Dong, X Blum, RS Poor, HV |
| author_sort | Ramezani Mayiami, M |
| collection | OXFORD |
| description | Learning the topology of a graph from available data is of great interest in many emerging applications. Some examples are social networks, internet of things networks (intelligent IoT and industrial IoT), biological connection networks, sensor networks and traffic network patterns. In this paper, a graph topology inference approach is proposed to learn the underlying graph structure from a given set of noisy multi-variate observations, which are modeled as graph signals generated from a Gaussian Markov Random Field (GMRF) process. A factor analysis model is applied to represent the graph signals in a latent space where the basis is related to the underlying graph structure. An optimal graph filter is also developed to recover the graph signals from noisy observations. In the final step, an optimization problem is proposed to learn the underlying graph topology from the recovered signals. Moreover, a fast algorithm employing the proximal point method has been proposed to solve the problem efficiently. Experimental results employing both synthetic and real data show the effectiveness of the proposed method in recovering the signals and inferring the underlying graph. |
| first_indexed | 2024-03-07T08:09:31Z |
| format | Journal article |
| id | oxford-uuid:5fdb25e8-7397-49a9-9eae-779d2d3218e5 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-04-09T03:56:00Z |
| publishDate | 2021 |
| publisher | Springer |
| record_format | dspace |
| spelling | oxford-uuid:5fdb25e8-7397-49a9-9eae-779d2d3218e52024-03-14T11:38:31ZBayesian topology learning and noise removal from network dataJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:5fdb25e8-7397-49a9-9eae-779d2d3218e5EnglishSymplectic ElementsSpringer2021Ramezani Mayiami, MHajimirsadeghi, MSkretting, KDong, XBlum, RSPoor, HVLearning the topology of a graph from available data is of great interest in many emerging applications. Some examples are social networks, internet of things networks (intelligent IoT and industrial IoT), biological connection networks, sensor networks and traffic network patterns. In this paper, a graph topology inference approach is proposed to learn the underlying graph structure from a given set of noisy multi-variate observations, which are modeled as graph signals generated from a Gaussian Markov Random Field (GMRF) process. A factor analysis model is applied to represent the graph signals in a latent space where the basis is related to the underlying graph structure. An optimal graph filter is also developed to recover the graph signals from noisy observations. In the final step, an optimization problem is proposed to learn the underlying graph topology from the recovered signals. Moreover, a fast algorithm employing the proximal point method has been proposed to solve the problem efficiently. Experimental results employing both synthetic and real data show the effectiveness of the proposed method in recovering the signals and inferring the underlying graph. |
| spellingShingle | Ramezani Mayiami, M Hajimirsadeghi, M Skretting, K Dong, X Blum, RS Poor, HV Bayesian topology learning and noise removal from network data |
| title | Bayesian topology learning and noise removal from network data |
| title_full | Bayesian topology learning and noise removal from network data |
| title_fullStr | Bayesian topology learning and noise removal from network data |
| title_full_unstemmed | Bayesian topology learning and noise removal from network data |
| title_short | Bayesian topology learning and noise removal from network data |
| title_sort | bayesian topology learning and noise removal from network data |
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