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» Discovering Large Dense Subgraphs in Massive Graphs
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PAKDD
2010
ACM
169views Data Mining» more  PAKDD 2010»
14 years 9 days ago
oddball: Spotting Anomalies in Weighted Graphs
Given a large, weighted graph, how can we find anomalies? Which rules should be violated, before we label a node as an anomaly? We propose the OddBall algorithm, to find such nod...
Leman Akoglu, Mary McGlohon, Christos Faloutsos
ICDM
2009
IEEE
125views Data Mining» more  ICDM 2009»
14 years 2 months ago
A Fully Automated Method for Discovering Community Structures in High Dimensional Data
—Identifying modules, or natural communities, in large complex networks is fundamental in many fields, including social sciences, biological sciences and engineering. Recently s...
Jianhua Ruan
STOC
2004
ACM
134views Algorithms» more  STOC 2004»
14 years 7 months ago
Approximate max-integral-flow/min-multicut theorems
We establish several approximate max-integral-flow / minmulticut theorems. While in general this ratio can be very large, we prove strong approximation ratios in the case where th...
Kenji Obata
KDD
2009
ACM
150views Data Mining» more  KDD 2009»
14 years 8 months ago
Large human communication networks: patterns and a utility-driven generator
Given a real, and weighted person-to-person network which changes over time, what can we say about the cliques that it contains? Do the incidents of communication, or weights on t...
Nan Du, Christos Faloutsos, Bai Wang, Leman Akoglu
CVPR
2008
IEEE
14 years 9 months ago
Articulated shape matching using Laplacian eigenfunctions and unsupervised point registration
Matching articulated shapes represented by voxel-sets reduces to maximal sub-graph isomorphism when each set is described by a weighted graph. Spectral graph theory can be used to...
Diana Mateus, Radu Horaud, David Knossow, Fabio Cu...