Scalable Dyadic Independence Models with Local and Global Constraints

14 Feb 2020Florian AdriaensAlexandru MaraJefrey LijffijtTijl De Bie

An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-trivial ERGs on large networks. By utilizing matrix block-approximation techniques, we propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) models, while being able to meaningfully model local information (degrees) as well as global information (clustering coefficient, assortativity, etc.).. (read more)

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