A0628
Title: Random walk models of network formation
Authors: Peter Orbanz - Columbia University (United States) [presenting]
Abstract: A class of network models are described that insert edges by connecting the starting and terminal vertices of a random walk on the network graph. Within the taxonomy of statistical network models, this class is distinguished by permitting the location of a new edge to explicitly depend on the structure of the graph, but being nonetheless statistically and computationally tractable. In the limit of infinite walk length, the model converges to an extension of the preferential attachment model. Theoretical properties will be discussed, such as power laws, and show that inference of model parameters is possible from a single graph generated by the model.
