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» On the Vulnerability of Large Graphs
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JCP
2008
171views more  JCP 2008»
15 years 6 months ago
Mining Frequent Subgraph by Incidence Matrix Normalization
Existing frequent subgraph mining algorithms can operate efficiently on graphs that are sparse, have vertices with low and bounded degrees, and contain welllabeled vertices and edg...
Jia Wu, Ling Chen
NN
2006
Springer
104views Neural Networks» more  NN 2006»
15 years 6 months ago
Local multidimensional scaling
Several bioinformatics data sets are naturally represented as graphs, for instance gene regulation, metabolic pathways, and proteinprotein interactions. The graphs are often large ...
Jarkko Venna, Samuel Kaski
SIGMOD
2008
ACM
191views Database» more  SIGMOD 2008»
16 years 6 months ago
Efficient aggregation for graph summarization
Graphs are widely used to model real world objects and their relationships, and large graph datasets are common in many application domains. To understand the underlying character...
Yuanyuan Tian, Richard A. Hankins, Jignesh M. Pate...
WEBI
2007
Springer
16 years 6 days ago
DENGRAPH: A Density-based Community Detection Algorithm
Detecting densely connected subgroups in graphs such as communities in social networks is of interest in many research fields. Several methods have been developed to find commun...
Tanja Falkowski, Anja Barth, Myra Spiliopoulou
APVIS
2001
15 years 7 months ago
Building Virtual Worlds with the Big-Bang Model
Visualisations implemented as virtual worlds can allow users to comprehend large graphs more effectively. Good 3D layout algorithms are an important element. Angle has been develo...
Neville Churcher, Alan Creek