Time-Varying Graph Learning for Data with Heavy-Tailed Distribution Article Swipe
Amirhossein Javaheri
,
Jiaxi Ying
,
Daniel P. Palomar
,
Farokh Marvasti
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2501.00606
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2501.00606
Graph models provide efficient tools to capture the underlying structure of data defined over networks. Many real-world network topologies are subject to change over time. Learning to model the dynamic interactions between entities in such networks is known as time-varying graph learning. Current methodology for learning such models often lacks robustness to outliers in the data and fails to handle heavy-tailed distributions, a common feature in many real-world datasets (e.g., financial data). This paper addresses the problem of learning time-varying graph models capable of efficiently representing heavy-tailed data. Unlike traditional approaches, we incorporate graph structures with specific spectral properties to enhance data clustering in our model. Our proposed method, which can also deal with noise and missing values in the data, is based on a stochastic approach, where a non-negative vector auto-regressive (VAR) model captures the variations in the graph and a Student-t distribution models the signal originating from this underlying time-varying graph. We propose an iterative method to learn time-varying graph topologies within a semi-online framework where only a mini-batch of data is used to update the graph. Simulations with both synthetic and real datasets demonstrate the efficacy of our model in analyzing heavy-tailed data, particularly those found in financial markets.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2501.00606
- https://arxiv.org/pdf/2501.00606
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406031669
All OpenAlex metadata
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https://openalex.org/W4406031669Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2501.00606Digital Object Identifier
- Title
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Time-Varying Graph Learning for Data with Heavy-Tailed DistributionWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-31Full publication date if available
- Authors
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Amirhossein Javaheri, Jiaxi Ying, Daniel P. Palomar, Farokh MarvastiList of authors in order
- Landing page
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https://arxiv.org/abs/2501.00606Publisher landing page
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https://arxiv.org/pdf/2501.00606Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2501.00606Direct OA link when available
- Concepts
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Graph, Distribution (mathematics), Environmental science, Econometrics, Computer science, Mathematics, Statistics, Combinatorics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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