Sciweavers

GD
2004
Springer
13 years 10 months ago
GraphML Transformation
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 ...
Ulrik Brandes, Christian Pich
ISI
2007
Springer
13 years 10 months ago
Detecting Anomalies in Graphs
Graph data represents relationships, connections, or affinities. Innocent relationships produce repeated, and so common, substructures in graph data. We present techniques for dis...
David B. Skillicorn
ICDM
2008
IEEE
106views Data Mining» more  ICDM 2008»
13 years 11 months ago
Metropolis Algorithms for Representative Subgraph Sampling
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....
Christian Hübler, Hans-Peter Kriegel, Karsten...
CIKM
2009
Springer
13 years 11 months ago
Frequent subgraph pattern mining on uncertain graph data
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...
Zhaonian Zou, Jianzhong Li, Hong Gao, Shuo Zhang
SAC
2009
ACM
13 years 11 months ago
Applying latent dirichlet allocation to group discovery in large graphs
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...
Keith Henderson, Tina Eliassi-Rad