# rMultiNet: An R Package For Multilayer Networks Analysis

arXiv: https://arxiv.org/abs/2302.04437 · HTML: https://mlsanity.com/paper/2302.04437/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 9 Feb 2023.

## Builds on (most cited first, 10 of 10)

- [Inference for multiple heterogeneous networks with a common invariant subspace](https://mlsanity.com/paper/1906.10026/) (2019)
- [Community Detection in Degree-Corrected Block Models](https://mlsanity.com/paper/1607.06993/) (2016)
- [Achieving Optimal Misclassification Proportion in Stochastic Block Model](https://mlsanity.com/paper/1505.03772/) (2015)
- [Layer aggregation and reducibility of multilayer interconnected networks](https://mlsanity.com/paper/1405.0425/) (2014)
- [Community Detection on Mixture Multi-layer Networks via Regularized Tensor Decomposition](https://mlsanity.com/paper/2002.04457/) (2020)
- [Spectral and matrix factorization methods for consistent community detection in multi-layer networks](https://mlsanity.com/paper/1704.07353/) (2017)
- [Latent Space Model for Higher-order Networks and Generalized Tensor Decomposition](https://mlsanity.com/paper/2106.16042/) (2021)
- [Global and Individualized Community Detection in Inhomogeneous Multilayer Networks](https://mlsanity.com/paper/2012.00933/) (2020)
- [Fast Network Community Detection with Profile-Pseudo Likelihood Methods](https://mlsanity.com/paper/2011.00647/) (2020)
- [Estimating a network from multiple noisy realizations](https://mlsanity.com/paper/1710.04765/) (2017)

## Built on by (most cited first, 1 of 1)

- [Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings](https://mlsanity.com/paper/2310.11161/) (2023)

## Abstract (arXiv)

This paper develops an R package rMultiNet to analyze multilayer network data. We provide two general frameworks from recent literature, e.g. mixture multilayer stochastic block model(MMSBM) and mixture multilayer latent space model(MMLSM) to generate the multilayer network. We also provide several methods to reveal the embedding of both nodes and layers followed by further data analysis methods, such as clustering. Three real data examples are processed in the package. The source code of rMultiNet is available at https://github.com/ChenyuzZZ73/rMultiNet.