rMultiNet: An R Package For Multilayer Networks Analysis
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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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 19 references that are in the map. First posted on arXiv 9 Feb 2023.
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10 of 19 references.
References · 10
- 2019Inference for multiple heterogeneous networks with a common invariant subspacecited by 39
- 2016Community Detection in Degree-Corrected Block Modelscited by 36
- 2015Achieving Optimal Misclassification Proportion in Stochastic Block Modelcited by 35
- 2014Layer aggregation and reducibility of multilayer interconnected networkscited by 34
- 2020Community Detection on Mixture Multi-layer Networks via Regularized Tensor Decompositioncited by 30
- 2017Spectral and matrix factorization methods for consistent community detection in multi-layer networkscited by 27
- 2021Latent Space Model for Higher-order Networks and Generalized Tensor Decompositioncited by 13
- 2020Global and Individualized Community Detection in Inhomogeneous Multilayer Networkscited by 11
- 2020Fast Network Community Detection with Profile-Pseudo Likelihood Methodscited by 8
- 2017Estimating a network from multiple noisy realizationscited by 7
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Citing papers per year: 2023: 1.
1 paper.