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Radalib

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

Description

Radalib is a library originally developed by Sergio Gómez (sergio.gomez@urv.cat) and Alberto Fernández (alberto.fernandez@urv.cat), which we have continuously been updating for our research within the Alephsys research group, led by Alex Arenas, at Universitat Rovira i Virgili (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
• 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 (sergio.gomez@urv.cat), Alberto Fernández (alberto.fernandez@urv.cat)

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) (ORCID) (Google Scholar) (GitHub)

• Sergio Gómez: Dept. Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona (Spain). (web) (email) (ORCID) (Google Scholar) (GitHub) (Twitter)