The efforts put into XML-related technologies have exciting consequences for XML-based graph data formats such as GraphML. We here give a systematic overview of the possibilities ...
Graph data represents relationships, connections, or affinities. Innocent relationships produce repeated, and so common, substructures in graph data. We present techniques for dis...
While data mining in chemoinformatics studied graph data with dozens of nodes, systems biology and the Internet are now generating graph data with thousands and millions of nodes....
Graph data are subject to uncertainties in many applications due to incompleteness and imprecision of data. Mining uncertain graph data is semantically different from and computat...
This paper introduces LDA-G, a scalable Bayesian approach to finding latent group structures in large real-world graph data. Existing Bayesian approaches for group discovery (suc...