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2025-gnn-evo-architecture

Graph Neural Networks: A unifying predictive model architecture for evolutionary applications

https://github.com/arcadia-science/2025-gnn-evo-architecture

Science Score: 67.0%

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    Found 6 DOI reference(s) in README
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    Links to: wiley.com
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Repository

Graph Neural Networks: A unifying predictive model architecture for evolutionary applications

Basic Info
  • Host: GitHub
  • Owner: Arcadia-Science
  • License: mit
  • Language: R
  • Default Branch: main
  • Size: 558 KB
Statistics
  • Stars: 0
  • Watchers: 0
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created about 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme License Citation

README.md

2025-gnn-evo-architecture

run with conda

Purpose

The analysis in this repo processes the literature review dataset from Borowiec et al., 2022 (data obtained here), and produces the visuals in Figure 1 of this repos associated pub, "Graph Neural Networks: A unifying predictive model architecture for evolutionary applications".

Installation and Setup

This repository uses conda to manage software environments and installations. You can find operating system-specific instructions for installing miniconda here. After installing conda and mamba, run the following command to create the pipeline run environment.

```{bash} mamba env create -n evognnperspective --file envs/environment.yml conda activate evognnperspective

Install arcadiathemeR package for plotting from github

Rscript install_arcadiathemer.R ```

Overview

Description of the folder structure

  1. mee313901-sup-0001-supinfo.txt: Tab-delimited data from Borowiec et al., 2022. Reformatted from the original excel file.
  2. plot_nn_evo_pub_trends.R: R-script used to process and plot NN usage trends as shown in Figure 1 of our associated pub.

Methods

To run analyses, simply call the following from the commandline.

Rscript plot_nn_evo_pub_trends.R

Compute Specifications

Analysis was originally carried out on a 2021 Macbook Pro with an Apple M1 Pro processor.

Owner

  • Name: Arcadia Science
  • Login: Arcadia-Science
  • Kind: organization
  • Location: United States of America

Citation (CITATION.cff) 

cff-version: 1.2.0
message: If you use this software, please cite the associated publication.
title: 'Graph neural networks: A unifying predictive model architecture for evolutionary
  applications'
doi: 10.57844/arcadia-e7kq-frwh
authors:
- family-names: Bell
  given-names: Audrey
  affiliation: Arcadia Science
  orcid: https://orcid.org/0009-0008-2270-1613
- family-names: Patton
  given-names: Austin H.
  affiliation: Arcadia Science
  orcid: https://orcid.org/0000-0003-1286-9005
- family-names: York
  given-names: Ryan
  affiliation: Arcadia Science
  orcid: https://orcid.org/0000-0002-1073-1494
preferred-citation:
  title: 'Graph neural networks: A unifying predictive model architecture for evolutionary
    applications'
  type: article
  doi: 10.57844/arcadia-e7kq-frwh
  authors:
  - family-names: Patton
    given-names: Austin H.
    affiliation: Arcadia Science
    orcid: https://orcid.org/0000-0003-1286-9005
  - family-names: York
    given-names: Ryan
    affiliation: Arcadia Science
    orcid: https://orcid.org/0000-0002-1073-1494
  year: 2025

GitHub Events

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  • Push event: 1
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  • Create event: 1
Last Year
  • Release event: 1
  • Watch event: 2
  • Push event: 1
  • Public event: 1
  • Create event: 1

Dependencies

.github/workflows/lint.yml actions
  • actions/checkout v3 composite
  • r-lib/actions/setup-r v2 composite
envs/environment.yml pypi