Casiraghi Applied Network Science (2019) 4:123 Applied Network Science https://doi.org/10.1007/s41109-019-0241-1 RESEARCH Open Access The block-constrained configuration model Giona Casiraghi Correspondence:
[email protected] Abstract Chair of Systems Design, ETH We provide a novel family of generative block-models for random graphs that naturally Zürich, Weinbergstrasse 56/58, incorporates degree distributions: the block-constrained configuration model. 8092 Zürich, Switzerland Block-constrained configuration models build on the generalized hypergeometric ensemble of random graphs and extend the well-known configuration model by enforcing block-constraints on the edge-generating process. The resulting models are practical to fit even to large networks. These models provide a new, flexible tool for the study of community structure and for network science in general, where modeling networks with heterogeneous degree distributions is of central importance. Keywords: Block model, Community structure, Random graphs, Configuration model, Network analysis, gHypEG Introduction Stochastic block-models (SBMs) are random models for graphs characterized by group, communities, or block structures. They are a generalization of the classical G(n, p) Erdos-˝ Rènyi model (1959), where vertices are separated into B different blocks, and different probabilities to create edges are then assigned to each block. This way, higher probabili- ties correspond to more densely connected groups of vertices, capturing the structure of clustered graphs (Fienberg et al. 1985; Holland et al. 1983;Peixoto2012). SBMs are specified by defining a B × B block-matrix of probabilities B such that each of ω its elements bibj is the probability of observing an edge between vertices i and j,wherebi denotes the block to which vertex i belongs.