  Networks arising from social, technological and natural domains exhibit rich
connectivity patterns and nodes in such networks are often labeled with
attributes or features. We address the question of modeling the structure of
networks where nodes have attribute information. We present a Multiplicative
Attribute Graph (MAG) model that considers nodes with categorical attributes
and models the probability of an edge as the product of individual attribute
link formation affinities. We develop a scalable variational expectation
maximization parameter estimation method. Experiments show that MAG model
reliably captures network connectivity as well as provides insights into how
different attributes shape the network structure.
