48 citations · 165 across the 24 of their papers we have counts for
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math.PR2017★ 9 cited
Mixing Time of Vertex-Weighted Exponential Random Graphs
Ryan DeMuse, Terry Easlick, Mei Yin
Exponential random graph models have become increasingly important in the study of modern networks ranging from social networks, economic networks, to biological networks. They see…
math.PR2017
Ground States for Exponential Random Graphs
Rajinder Mavi, Mei Yin
We propose a perturbative method to estimate the normalization constant in exponential random graph models as the weighting parameters approach infinity. As an application, we give…
math.PR2017★ 3 cited
Phase Transitions in Edge-Weighted Exponential Random Graphs: Near-Degeneracy and Universality
Ryan DeMuse, Danielle Larcomb, Mei Yin
Conventionally used exponential random graphs cannot directly model weighted networks as the underlying probability space consists of simple graphs only. Since many substantively i…