MUMA: A multiplex network analysis library

Abstract

Multiplex network model has been recently proposed as a mean to capture high level complexity in real-world interaction networks. This model, in spite of its simplicity, allows handling multi-relationnal, heterogeneous, dynamic and even attributed networks. However, it requiers redefining and adapting almost all basic metrics and algorithms generally used to analyse complex networks. In this work we present MUNA: a MUltiplex Network Analysis library that we have developed in both R and Python on top of igraph network analysis package. In its current version, MUNA provides primitives to build, edit and modify multiplex networks. It also provides a bunch of functions computing basic metrics on multiplex networks. However, the most interesting functionality provided by MUNA is probably the wide variety of available community detection algorithms. Actually, the library implements different approaches for community detection including: partition aggregation approaches, layer aggregation approaches and direct multiplex approaches such as the GenLouvain and MuxLicod algorithms. It also offers an extended list of multiplex community evaluation indexes.