Inference of dynamic interaction networks: A comparison between Lotka-Volterra and multivariate autoregressive models Article Swipe
Daniel V. Olivença
,
Jacob D. Davis
,
Eberhard O. Voit
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fbinf.2022.1021838
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fbinf.2022.1021838
Networks are ubiquitous throughout biology, spanning the entire range from molecules to food webs and global environmental systems. Yet, despite substantial efforts by the scientific community, the inference of these networks from data still presents a problem that is unsolved in general. One frequent strategy of addressing the structure of networks is the assumption that the interactions among molecular or organismal populations are static and correlative. While often successful, these static methods are no panacea. They usually ignore the asymmetry of relationships between two species and inferences become more challenging if the network nodes represent dynamically changing quantities. Overcoming these challenges, two very different network inference approaches have been proposed in the literature: Lotka-Volterra (LV) models and Multivariate Autoregressive (MAR) models. These models are computational frameworks with different mathematical structures which, nevertheless, have both been proposed for the same purpose of inferring the interactions within coexisting population networks from observed time-series data. Here, we assess these dynamic network inference methods for the first time in a side-by-side comparison, using both synthetically generated and ecological datasets. Multivariate Autoregressive and Lotka-Volterra models are mathematically equivalent at the steady state, but the results of our comparison suggest that Lotka-Volterra models are generally superior in capturing the dynamics of networks with non-linear dynamics, whereas Multivariate Autoregressive models are better suited for analyses of networks of populations with process noise and close-to linear behavior. To the best of our knowledge, this is the first study comparing LV and MAR approaches. Both frameworks are valuable tools that address slightly different aspects of dynamic networks.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fbinf.2022.1021838
- https://www.frontiersin.org/articles/10.3389/fbinf.2022.1021838/pdf
- OA Status
- gold
- Cited By
- 7
- References
- 70
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4312076797
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4312076797Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fbinf.2022.1021838Digital Object Identifier
- Title
-
Inference of dynamic interaction networks: A comparison between Lotka-Volterra and multivariate autoregressive modelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-22Full publication date if available
- Authors
-
Daniel V. Olivença, Jacob D. Davis, Eberhard O. VoitList of authors in order
- Landing page
-
https://doi.org/10.3389/fbinf.2022.1021838Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fbinf.2022.1021838/pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/articles/10.3389/fbinf.2022.1021838/pdfDirect OA link when available
- Concepts
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Autoregressive model, Inference, Computer science, Multivariate statistics, Population, Range (aeronautics), Econometrics, Artificial intelligence, Machine learning, Mathematics, Engineering, Aerospace engineering, Sociology, DemographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
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2025: 2, 2024: 2, 2023: 2, 2022: 1Per-year citation counts (last 5 years)
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70Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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