Triadic structures in multislice networks

Abstract

Networks provide a popular representation of complex data. Often, different types of relational measurements are taken on the same subjects. Such data can be represented as a \textit{multislice network}, a collection of networks on the same set of nodes, with connections between the different layers to be determined. For the analysis of multislice networks, we take inspiration from the analysis of simple networks, for which small subgraphs (motifs) have proven to be useful; motifs are even seen as building blocks of complex networks. A particular instance of a motif is a triangle, and while triangle counts are well understood for simple network models such as Erdős-Rényi random graphs, with i.i.d. distributed edges, even for simple multislice network models little is known about triangle counts. Here we address this issue by extending the analysis of triadic structures to multislice Erdős-Rényi networks. Again taking inspiration from the analysis of sparse Erdős-Rényi random graphs, we show that the distribution of triangles across multiple layers in a multislice Erdős-Rényi network can be well approximated by an appropriate Poisson distribution. This theoretical result opens the door to statistical goodness of fit tests for multislice networks.