Organize, Handle, and Explore Ecological Multilayer Networks

Data and analysis of ecological multilayer networks, including standardization of data structures and functions to convert between them. Includes an interactive multilayer network visualizer (beta, paper forthcoming), and a collection of 78 empirical ecological multilayer network datasets. This work was supported by research grant ISF (Israel Science Foundation) 1281/20 to Shai Pilosof. Noa Frydman (2023) .

:wave: About

This repository contains the code for the R package EMLN. EMLN standardizes workflows for creating, storing, and converting multilayer network data, and ships with a collection of empirical ecological multilayer datasets ready for analysis. It also provides interactive, browser-based visualization through its integration with MiRA, launched directly from R via plot_multilayer(). Although designed with ecological data in mind, EMLN is flexible and can handle data from other research domains.

:page_facing_up: Paper and citing

Frydman N, Freilikhman S, Talpaz I, Pilosof S. Practical guidelines and the EMLN R package for handling ecological multilayer networks. Methods in Ecology and Evolution. 2023. DOI:10.1111/2041-210X.14225. Please cite the paper when implementing the guidelines we describe or when using the package, this helps us a lot!

:package: Installation

EMLN is available on CRAN. installation is as follows:

install.packages("emln")

:globe_with_meridians: Website

Detailed explanations on workflows accompanied by examples for handling monolayer and multilayer data using emln are in: emln.ecomplab.com.

:bar_chart: Interactively visualizaing multilayer networks

EMLN integrates MiRA (Multilayer Interactive Rendering Application), a browser-based, installation-free visualizer launched from R via plot_multilayer(), or by exporting with multilayer_to_json() / multilayer_to_csv(). MiRA offers seven complementary modes — Network (3D), Map, Grid View, Layer View, Meta-Network, Dashboard, and Data — with interactive rotation, filtering, color/size mapping, and bipartite support, plus nine bundled empirical datasets.

If you use MiRA in your published research, please cite the MiRA preprint:

Nehoray SM, Bloch Y, Pilosof S (2026). Interactively visualizing biological multilayer networks using MiRA. arXiv:2605.09597 [cs.SI]. https://doi.org/10.48550/arXiv.2605.09597