Incorporating Assortativity and Degree Dependence into Scalable Network Models

Due to the recent availability of large complex networks, considerable analysis has focused on understanding and characterizing the properties of these networks. Scalable generative graph models focus on modeling distributions of graphs that match real world network properties and scale to large datasets. Much work has focused on modeling networks with a power law degree distribution, clustering, and small diameter. In network analysis, the assortativity statistic is defined as the correlation between the degrees of linked nodes in the network. The assortativity measure can distinguish between types of networks—social networks commonly exhibit positive assortativity, in contrast to biological or technological networks that are typically disassortative. Despite this, little work has focused on scalable graph models that capture assortativity in networks. The contributions of our work are twofold. First, we prove that an unbounded number of pairs of networks exist with the same degre...

• Type: Journal
• Year: 2015
• Where: AAAI
• Authors: Stephen Mussmann, John Moore, Joseph John Pfeiffer III, Jennifer Neville
• Keywords: AAAI 2015, Intelligent Agents