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» On the Vulnerability of Large Graphs
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GD
2006
Springer
14 years 1 months ago
Eigensolver Methods for Progressive Multidimensional Scaling of Large Data
We present a novel sampling-based approximation technique for classical multidimensional scaling that yields an extremely fast layout algorithm suitable even for very large graphs....
Ulrik Brandes, Christian Pich
KDD
2006
ACM
164views Data Mining» more  KDD 2006»
14 years 10 months ago
Sampling from large graphs
Given a huge real graph, how can we derive a representative sample? There are many known algorithms to compute interesting measures (shortest paths, centrality, betweenness, etc.)...
Jure Leskovec, Christos Faloutsos
ICDE
2008
IEEE
190views Database» more  ICDE 2008»
14 years 11 months ago
Efficient Query Processing in Large Traffic Networks
Abstract-- We present an original graph embedding to speedup distance-range and k-nearest neighbor queries on static and/or dynamic objects located on a (weighted) graph. Our metho...
Hans-Peter Kriegel, Matthias Renz, Peer Kröge...
COLT
2004
Springer
14 years 3 months ago
Regularization and Semi-supervised Learning on Large Graphs
We consider the problem of labeling a partially labeled graph. This setting may arise in a number of situations from survey sampling to information retrieval to pattern recognition...
Mikhail Belkin, Irina Matveeva, Partha Niyogi
GD
2004
Springer
14 years 3 months ago
QUOGGLES: Query On Graphs - A Graphical Largely Extensible System
We describe the query and data processing language QUOGGLES which is particularly designed for the application on graphs. It uses a pipeline-like technique known from command line ...
Paul Holleis, Franz-Josef Brandenburg