Random Graph Models with Hidden Color
Bo Soderberg

TL;DR
This paper introduces a generalized framework for random graph models using hidden, unobservable vertex or stub attributes called colors, enabling complex correlation structures and broadening the scope of graph modeling.
Contribution
It presents a unifying formalism for extending classic random graph models with hidden color variables that influence edge probabilities and correlations.
Findings
Provides a flexible method to incorporate hidden attributes in graph models.
Enables modeling of complex edge correlation structures.
Unifies various random graph models under a common framework.
Abstract
We demonstrate how to generalize two of the most well-known random graph models, the classic random graph, and random graphs with a given degree distribution, by the introduction of hidden variables in the form of extra degrees of freedom, color, applied to vertices or stubs (half-edges). The color is assumed unobservable, but is allowed to affect edge probabilities. This serves as a convenient method to define very general classes of models within a common unifying formalism, and allowing for a non-trivial edge correlation structure.
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Taxonomy
TopicsComplex Network Analysis Techniques · Bayesian Modeling and Causal Inference · Stochastic processes and statistical mechanics
