Update

Co-Authored-By: Claudio Moroni <43729990+ClaudMor@users.noreply.github.com>
Co-Authored-By: Pietro Monticone <38562595+pitmonticone@users.noreply.github.com>

Author: 3 people

README.md

@@ -31,7 +31,7 @@ Multiple theoretical frameworks have been proposed to formally subsume all insta
 
 Multilayer graphs have been adopted to model the structure and dynamics of a wide spectrum of high-dimensional, non-linear, multi-scale, time-dependent complex systems including physical, chemical, biological, neuronal, socio-technical, epidemiological, ecological and economic networks ([Cozzo et al. (2013)](https://doi.org/10.1103/physreve.88.050801); [Granell et al. (2013)](https://doi.org/10.1103/physrevlett.111.128701); [Massaro and Bagnoli (2014)](https://doi.org/10.1103/physreve.90.052817); [Estrada and Gomez-Gardenes (2014)](https://doi.org/10.1103/physreve.89.042819); [Azimi-Tafreshi (2016)](https://doi.org/10.1103/physreve.93.042303); [Baggio et al. (2016)](https://doi.org/10.1073/pnas.1604401113); [DeDomenico et al. (2016)](https://doi.org/10.1038/nphys3865); [Amato et al. (2017)](https://doi.org/10.1038/s41598-017-06933-2); [DeDomenico (2017)](https://doi.org/10.1093/gigascience/gix004); [Pilosof et al. (2017)](https://doi.org/10.1038/s41559-017-0101); [de Arruda et al. (2017)](https://doi.org/10.1103/physrevx.7.011014); [Gosak et al. (2018)](https://doi.org/10.1016/j.plrev.2017.11.003); [Soriano-Panos et al. (2018)](https://doi.org/10.1103/physrevx.8.031039); [Timteo et al. (2018)](https://doi.org/10.1038/s41467-017-02658-y); [Buldú et al. (2018)](https://doi.org/10.1162/netn_a_00033); [Lim et al. (2019)](https://doi.org/10.1038/s41598-019-39243-w); [Mangioni et al. (2020)](https://doi.org/10.1109/tnse.2018.2871726); [Aleta et al. (2020)](https://doi.org/10.1038/s41562-020-0931-9); [Aleta et al. (2022)](https://doi.org/10.1073/pnas.2112182119)). 
 
-MultilayerGraphs.jl is an integral part of the [JuliaGraphs](https://github.com/JuliaGraphs) ecosystem extending [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl) so all the methods and metrics exported by Graphs.jl work for multilayer graphs, but due to the special nature of multilayer graphs the package features a peculiar implementation that maps a standard integer-labelled vertex representation to a more user-friendly framework exporting all the objects an experienced practitioner would expect such as nodes (`Node`), vertices (`MultilayerVertex`), layers (`Layer`), interlayer (`Interlayer`), etc.
+MultilayerGraphs.jl is an integral part of the [JuliaGraphs](https://github.com/JuliaGraphs) ecosystem extending [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl) so all the methods and metrics exported by Graphs.jl work for multilayer graphs, but due to the special nature of multilayer graphs the package features a peculiar implementation that maps a standard integer-labelled vertex representation to a more user-friendly framework exporting all the objects an experienced practitioner would expect such as nodes (`Node`), vertices (`MultilayerVertex`), layers (`Layer`), interlayers (`Interlayer`), etc.
 
 MultilayerGraphs.jl features multilayer-specific methods and metrics including the global clustering coefficient, the overlay clustering coefficient, the multilayer eigenvector centrality, the multilayer modularity and the Von Neumann entropy.
 

paper/JuliaCon2023.md

@@ -24,7 +24,7 @@ Multilayer graphs have been adopted to model the structure and dynamics of a wid
 
 MultilayerGraphs.jl is integrated within the **JuliaGraphs** ecosystem extending [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl) with several multilayer-specific methods and metrics including the multilayer eigenvector centrality, the multilayer modularity and the Von Newman entropy. 
 
-The package features an implementation that maps a standard integer-labelled vertex representation to a more user-friendly framework exporting all the objects a practitioner would expect such as nodes (`Node`), vertices (`MultilayerVertex`), layers (`Layer`), interlayer (`Interlayer`), etc.
+The package features an implementation that maps a standard integer-labelled vertex representation to a more user-friendly framework exporting all the objects a practitioner would expect such as nodes (`Node`), vertices (`MultilayerVertex`), layers (`Layer`), interlayers (`Interlayer`), etc.
 
 The two main data structures are `MultilayerGraph` and `MultilayerDiGraph`: collections of layers connected through interlayers. 
 

paper/paper.md

@@ -43,7 +43,7 @@ Formally, a multilayer graph can be defined as a triple $G=(V,E,L)$, where:
 
 Each layer $\ell$ in $L$ is a tuple $(V_\ell, E_\ell)$, where $V_\ell$ is a subset of $V$ that represents the vertices within that layer, and $E_\ell$ is a subset of $E$ that represents the edges within that layer.
 
-MultilayerGraphs.jl is an integral part of the [JuliaGraphs](https://github.com/JuliaGraphs) ecosystem extending Graphs.jl [@Graphs2021] so all the methods and metrics exported by Graphs.jl work for multilayer graphs, but due to the special nature of multilayer graphs the package features a peculiar implementation that maps a standard integer-labelled vertex representation to a more user-friendly framework exporting all the objects an experienced practitioner would expect such as nodes (`Node`), vertices (`MultilayerVertex`), layers (`Layer`), interlayer (`Interlayer`), etc.
+MultilayerGraphs.jl is an integral part of the [JuliaGraphs](https://github.com/JuliaGraphs) ecosystem extending Graphs.jl [@Graphs2021] so all the methods and metrics exported by Graphs.jl work for multilayer graphs, but due to the special nature of multilayer graphs the package features a peculiar implementation that maps a standard integer-labelled vertex representation to a more user-friendly framework exporting all the objects an experienced practitioner would expect such as nodes (`Node`), vertices (`MultilayerVertex`), layers (`Layer`), interlayers (`Interlayer`), etc.
 
 MultilayerGraphs.jl features multilayer-specific methods and metrics including the global clustering coefficient, the overlay clustering coefficient, the multilayer eigenvector centrality, the multilayer modularity and the Von Neumann entropy.