Relational models for generating labeled real-world graphs

Abstract Analyzing and understanding the structure of social networks and other real-world graphs has become a major area of research in the,eld of data mining. An important problem setting is the creation of realistic synthetic graphs that resemble real-world social networks. While a range of ecient,algorithms for this task have been proposed, current methods solely take the network topology into account ignoring any node labels. By applying concepts from relational learning we propose a probabilistic approach to synthetic graph generation with node labels. 1 Generation of real-world graphs