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» Colibri: fast mining of large static and dynamic graphs
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KDD
2008
ACM
165views Data Mining» more  KDD 2008»
9 years 4 months ago
Colibri: fast mining of large static and dynamic graphs
Low-rank approximations of the adjacency matrix of a graph are essential in finding patterns (such as communities) and detecting anomalies. Additionally, it is desirable to track ...
Hanghang Tong, Spiros Papadimitriou, Jimeng Sun, P...
ICDM
2010
IEEE
158views Data Mining» more  ICDM 2010»
8 years 2 months ago
Patterns on the Connected Components of Terabyte-Scale Graphs
How do connected components evolve? What are the regularities that govern the dynamic growth process and the static snapshot of the connected components? In this work, we study pat...
U. Kang, Mary McGlohon, Leman Akoglu, Christos Fal...
PKDD
2009
Springer
129views Data Mining» more  PKDD 2009»
8 years 11 months ago
RTG: A Recursive Realistic Graph Generator Using Random Typing
We propose a new, recursive model to generate realistic graphs, evolving over time. Our model has the following properties: it is (a) flexible, capable of generating the cross pro...
Leman Akoglu, Christos Faloutsos
IWPC
2006
IEEE
8 years 10 months ago
Evolution Storyboards: Visualization of Software Structure Dynamics
Large software systems have a rich development history. Mining certain aspects of this rich history can reveal interesting insights into the system and its structure. Previous app...
Dirk Beyer, Ahmed E. Hassan
KDD
2009
ACM
163views Data Mining» more  KDD 2009»
9 years 5 months ago
Large-scale graph mining using backbone refinement classes
We present a new approach to large-scale graph mining based on so-called backbone refinement classes. The method efficiently mines tree-shaped subgraph descriptors under minimum f...
Andreas Maunz, Christoph Helma, Stefan Kramer
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