Multiplexity analysis of networks using multigraph representations

Abstract Multivariate networks comprising several compositional and structural variables can be represented as multigraphs by various forms of aggregations based on vertex attributes. We propose a framework to perform exploratory confirmatory multiplexity analysis aggregated in order find relevant associations between edge The exploration is performed comparing frequencies the different edges within categories, while using derived complexity or statistics under random multigraph models. These are defined distribution multiplicities provide information covariation dependencies given presented approach highlights need further analyse model with respect entrainment. illustrate applying it well known multivariate network dataset which has previously been analysed context multiplexity.