Nicholas Sieger, University of California, San Diego

Abstract

We introduce a random graph model for clustering graphs with a given degree sequence. Unlike many previous random graph models, we incorporate clustering effects into the model. We show that random clustering graphs can construct graphs with a power-law expected degree sequence, small diameter, and any desired clustering coefficient. Our results follow from a general theorem on subgraph counts which may be of independent interest. 

See: https://sites.google.com/view/rutgersdmseminar