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https://github.com/sergio-gomez/Radalib

Ada library and tools for the analysis of Complex Networks and more
https://github.com/sergio-gomez/Radalib

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Ada library and tools for the analysis of Complex Networks and more

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README 

        
[![DOI](https://zenodo.org/badge/46231296.svg)](https://zenodo.org/badge/latestdoi/46231296)



# Radalib



Ada library and tools for the analysis of Complex Networks and more.



## Description



**Radalib** is a library originally developed by [*Sergio Gómez*](https://webs-deim.urv.cat/~sergio.gomez) ([email protected]) and *Alberto Fernández* ([email protected]), which we have continuously been updating for our research within the [*Alephsys*](https://webs-deim.urv.cat/~alephsys) research group, led by [*Alex Arenas*](https://webs-deim.urv.cat/~alexandre.arenas), at [*Universitat Rovira i Virgili*](https://www.urv.cat/en/) (URV), Tarragona, since 2004. Previous experience showed that the continuous reuse of code was a painful task, thus we decided to be more structured and separate general purpose code (e.g. manipulation of networks and partitions) from the specific details of particular applications (e.g. Monte Carlo simulation of epidemic spreading). The result was the development of a general purpose library, mostly devoted to complex networks, and developed around abstract data types. This means the types are defined as "private", with public subprograms operating on them and encapsulating their implementations, thus allowing for future enhancements without having to modify the programs already using them. We could have used object oriented programming, but we believe polymorphism and inheritance are basically useless for this kind of applications.



The selected language was Ada, for several reasons: performance (it is a compiled language, not interpreted), readable code, support for abstract data types, strict data type system (allows catching many errors at compile time), advanced support of generics, high level support for concurrent programming (just in case it is needed), availability of high quality compilers for the main platforms (*Windows*, *Linux*, *MacOS*), and the confidence in your code when using it. The main drawback was the absence of code from other people we could reuse, but that was not a problem since we wanted full control and detailed understanding of every line of code used for our research.



**Radalib** is structured in three parts: *source* (the library itself), *test* and *tools*. *Tools* are programs which solve a certain problem, e.g. community detection, partitions comparison, network properties, connected components, file format conversion, etc., and which are basically mere interfaces to functionalities given by the library. The requests to make public implementations of some of the algorithms presented in our scientific papers led to the publication of **Radatools**, which are just executables for *Windows*, *Linux* and *MacOS* of some of the **Radalib** tools.



## Webs



- **Radalib** home: https://webs-deim.urv.cat/~sergio.gomez/radalib.php and [GitHub](https://github.com/sergio-gomez/Radalib)

- **Radatools** home: https://webs-deim.urv.cat/~sergio.gomez/radatools.php



## Structure



**Radalib** is distributed in a single compressed file containing the following folders or directories:



- `radalib`

    Root of **Radalib**, containing README, LICENSE, info, version and howto files.



- `radalib/docs`

    Mathematical description of all modularity types available in **Radalib**.



- `radalib/source`

    All the packages which form the library, and scripts for their compilation.



- `radalib/test`

    Test programs for most of the packages in the source folder, each one with a script for its compilation and execution, and some test data files.



- `radalib/tools`

    Programs which take advantage of the source packages, mainly for the analysis of complex networks. There are also scripts for their compilation and execution, and test data files.



- `radalib/compiled`

    Object files obtained from the compilation of the source packages. By default, they are for *Windows*; they need to be rebuilt when working in other platforms.



- `radalib/maintenance`

    Scripts to simplify the upgrade and installation of **Radalib** in the different platforms.



The size of **Radalib** at version `radalib-20230326-194808` is:



- Code:     260 Ada files

- Files:    703 files

- Source: 66450 lines of Ada code



## Compilation



**Radalib** has been programmed using the Ada language, and with the aid of the GNAT Ada GPL Edition compilers from Adacore. Follow the indications in your corresponding "howto" file to install GNAT and compile **Radalib**:



- *Windows*: `radalib-howto-windows.txt`

- *Linux*: `radalib-howto-linux.txt`

- *MacOS*: `radalib-howto-mac.txt`



## Radalib library



The main packages and sets of packages in **Radalib** are:



- `Graphs`

    Graph type for weighted directed or undirected networks. Based on vectors of adjacency lists. Supposes the number of vertices is fixed. There are child packages for Algorithms, Modularities, Operations, Properties and Multilayer algorithms. The Graph Structure defines a public data type for fast graph access (e.g. in Monte Carlo simulations).



- `Finite_Disjoint_Lists`

    List_Of_Lists type used to handle general purpose partitions. Very efficient for most of the operations.



- `Disjoint_Sets`

    Disjoint_Set type used to handle partitions optimized for additive percolation processes.



- `Contingency_Tables`

    Contingency_Table type to compare partitions in List_Of_Lists form.



- `Linked_Lists`

    Linked_List type implementing doubly linked lists.



- `Stacks`

    Stack type for LIFO storage.



- `Queues`

    Queue type for FIFO storage.



- `Minheaps`

    Minheap type implementing binary heaps.



- `Trees`

    Tree type used to handle hierarchical structures.



- `Dendrograms`

    Dendrogram type is a particular case of Tree in which nodes have several additional properties, the most relevant being a height.



- `Modularities` and `Modularity_Optimization`

    Modularity type and algorithms for its optimization.



- `Utils` and `Arrays`

    Several packages with general purpose types and subprograms, with emphasys on one- and two-dimensional dynamic objects, strings, input-output and files manipulation.



- `Random_Numbers`

    Several random number generators.



- `Histograms`

    Histogram type for the calculation of linear and logarithmic histograms.



- `Statistics`

    Calculation of the main statistics measures.



- `Eps_Plots` and `Eps_Utils`

    Utils for the direct generation of EPS plots.



- `Chrono_Utils`

    Chronometer type to measure elapsed times.



Many of the packages are just instantiations of the generic packages above for elements of simple types such as Integer, Float, Double or String.



## Radalib tools



The tools in **Radalib** are:



- `Communities_Detection`

    Community detection in complex networks by optimization of modularity, using the following heuristics: (h) exhaustive, (t) tabu, (e) extremal, (s) spectral, (l) louvain, (f) fast, \(r\) reposition, (b) fine-tuning based on tabu.



- `Communities_Network`

    Given a network and a partition, returns the weighted network of communities.



- `Compare_Partitions`

    Calculate similarity and dissimilarity indices between two partitions.



- `Connected_Subgraphs`

    Split a network into its (weak or strong) connected components.



- `Convert_Clu_To_Lol`

    Convert a partition in Pajek format (*.clu) into a partition in our Lol format.



- `Convert_Lol_To_Clu`

    Convert a partition in our Lol format into a partition in Pajek format (*.clu).



- `Data_Statistics`

    Calculate statistics of rows or columns in a data file.



- `Data_To_Correlations`

    Calculate the correlations network of a data file.



- `Data_To_Proximities`

    Calculate many types of proximities (distances or similarities) between rows or columns in a data file.



- `Extract_Subgraphs`

    Create subgraphs of a graph.



- `Hierarchical_Clustering`

    Agglomerative hierarchical clustering with multidendrograms and binary dendrograms.



- `Links_Info`

    Calculate the degrees and strengths of the nodes attached to each link in a network.



- `List_To_Net`

    Convert a network in list format to Pajek format (*.net).



- `Matrix_To_List`

    Convert a matrix to list format.



- `Matrix_To_Net`

    Convert a network in matrix format to Pajek format (*.net).



- `Mesoscales_Detection`

    Mesoscales detection in complex networks by optimization of modularity for variable common self-loops.



- `Mesoscales_Fine_Tuning`

    Fine Tuning of the mesoscales obtained with Mesoscales_Detection.



- `Modularity_Calculation`

    Calculate the modularity of a partition of a network, detailing the contributions of individual nodes and communities.



- `Multiplex_Aggregate`

    Calculate the aggregate network of a multiplex network.



- `Multiplex_Extract_Layers`

    Extract the layers of a multiplex network.



- `Net_To_List`

    Convert a network in Pajek format (*.net) to list format.



- `Net_To_Matrix`

    Convert a network in Pajek format (*.net) to matrix format.



- `Network_Properties`

    Calculate many properties of a network, including connectedness, degrees, strengths, clustering coefficients, assortativities, path lengths, efficiencies, diameters, entropies and betweenness. Handles all kinds of networks, even weighted, directed and signed.



- `Reformat_Partitions`

    Reformat partitions in Pajek and Lol formats changing nodes' indices by nodes' names.



- `Size_Reduction`

    Elimination of simple and triangular 'hairs' of a network to speed-up modularity optimization.



- `Size_Reduction_Lol_Expand`

    Convert a partition of a sized reduced network into a partition of the original network.



- `Sort_Nodes`

    Sort nodes of a network randomly or according to degree.



- `Spanning_Tree`

    Calculate the minimum and maximum spanning tree of a graph.



- `Symmetrize_Network`

    Symmetrization of a directed graph.



## License



**Radalib**, Copyright (c) 2023 by *Sergio Gómez* ([email protected]), *Alberto Fernández* ([email protected])



This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 2.1 as published by the Free Software Foundation.



This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.



You should have received a copy of the GNU Lesser General Public License along with this library (see LICENSE.txt); if not, see https://www.gnu.org/licenses/



## Acknowledgements



We thank *Javier Borge-Holthoefer* for his important contributions to **Radalib**, *Clara Granell* and *Pau Erola* for their influence in its development, *Albert Solé-Ribalta* for discovering and helping to solve some bugs, and *Alex Arenas* for leading the *Alephsys* research group at *Universitat Rovira i Virgili* (URV), Tarragona, in which all this software has become useful for our research.



## Authors



- **Alberto Fernández**: Dept. Enginyeria Química, Universitat Rovira i Virgili, Tarragona (Spain). ([email](mailto:[email protected]?subject=[mdendro])) ([ORCID](https://orcid.org/0000-0002-1241-1646)) ([Google Scholar](https://scholar.google.es/citations?user=AbH4r0IAAAAJ)) ([GitHub](https://github.com/albyfs))



- **Sergio Gómez**: Dept. Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona (Spain). ([web](https://webs-deim.urv.cat/~sergio.gomez/)) ([email](mailto:[email protected]?subject=[mdendro])) ([ORCID](https://orcid.org/0000-0003-1820-0062)) ([Google Scholar](https://scholar.google.es/citations?user=ETrjkSIAAAAJ)) ([GitHub](https://github.com/sergio-gomez)) ([Twitter](https://twitter.com/SergioGomezJ))