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Signed networks, i.e., networks with positive and negative edges, commonlyarise in various domains from social media to epidemiology. Modeling signednetworks has many practical applications, including the creation of syntheticdata sets for experiments where obtaining real data is difficult. Influentialprior works proposed and studied various graph topology models, as well as theproblem of selecting the most fitting model for different application domains.However, these topology models are typically unsigned.  In this work, we pose a novel Maximum-Likelihood-based optimization problemfor modeling signed networks given their topology and showcase it in thecontext of gene regulation. Regulatory interactions of genes play a key role inorganism development, and when broken can lead to serious organismabnormalities and diseases. Our contributions are threefold: First, we design anew class of signage models for a given topology. Based on the parametersetting, we discuss its biological interpretations for gene regulatory networks(GRNs). Second, we design algorithms computing the Maximum Likelihood --depending on the parameter setting, our algorithms range from closed-formexpressions to MCMC sampling. Third, we evaluated the results of our algorithmson synthetic datasets and real-world large GRNs. Our work can lead to theprediction of unknown gene regulations, the generation of biologicalhypotheses, and realistic GRN benchmark datasets.

GRASMOS: Graph Signage Model Selection for Gene Regulatory Networks