Relational models for generating labeled real-world graphs

Item Type:	Conference or Workshop Item
Title:	Relational models for generating labeled real-world graphs
Creators Name:	Lippert, C. and Shervashidze, N. and Stegle, O.
Abstract:	Analyzing and understanding the structure of social networks and other real-world graphs has become a major area of research in the field of data mining. An important problem setting is the creation of realistic synthetic graphs that resemble realworld social networks. While a range of efficient algorithms for this task have been proposed, current methods solely take the network topology into account ignoring any node labels. We propose a probabilistic approach to synthetic graph generation with node labels, building on concepts from relational learning.
Keywords:	Synthetic Graph Generation, Statistical Relational Learning, Infinite Relational Models
Date:	2009

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