A differentially private generative model, C-AGM, synthesizes attributed social graphs that preserve community structure, degree distributions, and clustering coefficients better than prior private graph models.
Community detection in networks: Modularity optimization and maximum likelihood are equivalent
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abstract
We demonstrate an exact equivalence between two widely used methods of community detection in networks, the method of modularity maximization in its generalized form which incorporates a resolution parameter controlling the size of the communities discovered, and the method of maximum likelihood applied to the special case of the stochastic block model known as the planted partition model, in which all communities in a network are assumed to have statistically similar properties. Among other things, this equivalence provides a mathematically principled derivation of the modularity function, clarifies the conditions and assumptions of its use, and gives an explicit formula for the optimal value of the resolution parameter.
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Publishing Community-Preserving Attributed Social Graphs with a Differential Privacy Guarantee
A differentially private generative model, C-AGM, synthesizes attributed social graphs that preserve community structure, degree distributions, and clustering coefficients better than prior private graph models.