Using isotope composition and other node attributes to predict edges in fish trophic networks

DOI10.1016/j.spl.2018.06.001zbMath1409.92268OpenAlexW2807372376MaRDI QIDQ1726756

Vyacheslav Lyubchich, Ryan J. Woodland

Publication date: 20 February 2019

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2018.06.001

zbMATH Keywords

random forestrandom networkfood webexponential random graph modeldeep neural networknode feature

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of graph theory (05C90) Learning and adaptive systems in artificial intelligence (68T05) Ecology (92D40) Systems biology, networks (92C42)

Uses Software

• ergm
• Ecosim
• ElemStatLearn
• darch
• ranger
• Ecopath with Ecosim

Cites Work

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• Modeling social networks from sampled data
• Estimating network degree distributions under sampling: an inverse problem, with applications to monitoring social media networks
• Statistical Learning from a Regression Perspective
• Reducing the Dimensionality of Data with Neural Networks
• Statistical Analysis of Network Data with R
• A Fast Learning Algorithm for Deep Belief Nets
• Random forests

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