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Title: Exponential-family random graph models with functional network parameters Authors:  Kevin Lee - Western Michigan University (United States) [presenting]
Amal Agarwal - Penn State University (United States)
Lingzhou Xue - Penn State University (United States)
Abstract: Dynamic networks are a general language for describing time-evolving complex systems, and have long been an interesting research area. It is a fundamental research question to model time varying network parameters. However, due to difficulties in modeling functional network parameters, there is little progress in the current literature to effectively model time varying network parameters. We consider the situation in which network parameters are univariate nonparametric functions instead of constants. Using a kernel regression technique, we introduce a novel unified procedure to effectively estimate those functional network parameters in the exponential-family random graph models. Moreover, by adopting the finite mixture models, we extend our model to mixture of exponential-family random graph models with functional network parameters, which simultaneously allows both modeling and detecting communities for the dynamic networks. The power of our method is demonstrated by simulation studies and real-world applications.