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» ORIGAMI: Mining Representative Orthogonal Graph Patterns
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KDD
2007
ACM
148views Data Mining» more  KDD 2007»
14 years 8 months ago
Detecting research topics via the correlation between graphs and texts
In this paper we address the problem of detecting topics in large-scale linked document collections. Recently, topic detection has become a very active area of research due to its...
Yookyung Jo, Carl Lagoze, C. Lee Giles
OOPSLA
2007
Springer
14 years 2 months ago
No ifs, ands, or buts: uncovering the simplicity of conditionals
Schematic tables are a new representation for conditionals. Roughly a cross between decision tables and data flow graphs, they represent computation and decision-making orthogona...
Jonathan Edwards
KDD
2006
ACM
198views Data Mining» more  KDD 2006»
14 years 8 months ago
Event detection from evolution of click-through data
Previous efforts on event detection from the web have focused primarily on web content and structure data ignoring the rich collection of web log data. In this paper, we propose t...
Qiankun Zhao, Tie-Yan Liu, Sourav S. Bhowmick, Wei...
KDD
2006
ACM
164views Data Mining» more  KDD 2006»
14 years 8 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
WSDM
2009
ACM
187views Data Mining» more  WSDM 2009»
14 years 3 months ago
Speeding up algorithms on compressed web graphs
A variety of lossless compression schemes have been proposed to reduce the storage requirements of web graphs. One successful approach is virtual node compression [7], in which of...
Chinmay Karande, Kumar Chellapilla, Reid Andersen