Source: libleidenalg Section: science Priority: optional Maintainer: Debian Med Packaging Team Uploaders: Étienne Mollier Build-Depends: debhelper-compat (= 13), cmake, libigraph-dev Standards-Version: 4.6.2 Vcs-Browser: https://salsa.debian.org/med-team/libleidenalg Vcs-Git: https://salsa.debian.org/med-team/libleidenalg.git Homepage: https://github.com/vtraag/libleidenalg Rules-Requires-Root: no Package: liblibleidenalg1 Architecture: any Depends: ${shlibs:Depends}, ${misc:Depends} Multi-Arch: same Section: libs Description: implementation of the Leiden algorithm in C++ - library This package implements the Leiden algorithm in C++. It relies on igraph for it to function. Besides the relative flexibility of the implementation, it also scales well, and can be run on graphs of millions of nodes (as long as they can fit in memory). The core class is Optimiser which finds the optimal partition using the Leiden algorithm, which is an extension of the Louvain algorithm for a number of different methods. The methods currently implemented are (1) modularity, (2) Reichardt and Bornholdt's model using the configuration null model and the Erdös-Rényi null model, (3) the Constant Potts model (CPM), (4) Significance, and finally (5) Surprise. In addition, it supports multiplex partition optimisation allowing community detection on for example negative links or multiple time slices. There is the possibility of only partially optimising a partition, so that some community assignments remain fixed. It also provides some support for community detection on bipartite graphs. . This package contains the library. Package: liblibleidenalg-dev Architecture: any Multi-Arch: same Depends: liblibleidenalg1 (= ${binary:Version}), ${misc:Depends} Section: libdevel Description: implementation of the Leiden algorithm in C++ - headers This package implements the Leiden algorithm in C++. It relies on igraph for it to function. Besides the relative flexibility of the implementation, it also scales well, and can be run on graphs of millions of nodes (as long as they can fit in memory). The core class is Optimiser which finds the optimal partition using the Leiden algorithm, which is an extension of the Louvain algorithm for a number of different methods. The methods currently implemented are (1) modularity, (2) Reichardt and Bornholdt's model using the configuration null model and the Erdös-Rényi null model, (3) the Constant Potts model (CPM), (4) Significance, and finally (5) Surprise. In addition, it supports multiplex partition optimisation allowing community detection on for example negative links or multiple time slices. There is the possibility of only partially optimising a partition, so that some community assignments remain fixed. It also provides some support for community detection on bipartite graphs. . This package contains the C++ development files. Most people will find it easier to work with the Python interface provided by python3-leidenalg.