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KDD
2005
ACM
140views Data Mining» more  KDD 2005»
14 years 9 months ago
Graphs over time: densification laws, shrinking diameters and possible explanations
How do real graphs evolve over time? What are "normal" growth patterns in social, technological, and information networks? Many studies have discovered patterns in stati...
Jure Leskovec, Jon M. Kleinberg, Christos Faloutso...
SDM
2009
SIAM
138views Data Mining» more  SDM 2009»
14 years 6 months ago
ShatterPlots: Fast Tools for Mining Large Graphs.
Graphs appear in several settings, like social networks, recommendation systems, computer communication networks, gene/protein biological networks, among others. A deep, recurring...
Ana Paula Appel, Andrew Tomkins, Christos Faloutso...
SDM
2009
SIAM
192views Data Mining» more  SDM 2009»
14 years 6 months ago
Mining Cohesive Patterns from Graphs with Feature Vectors.
The increasing availability of network data is creating a great potential for knowledge discovery from graph data. In many applications, feature vectors are given in addition to g...
Arash Rafiey, Flavia Moser, Martin Ester, Recep Co...
RECSYS
2009
ACM
14 years 3 months ago
Ordering innovators and laggards for product categorization and recommendation
Different buyers exhibit different purchasing behaviors. Some rush to purchase new products while others tend to be more cautious, waiting for reviews from people they trust. In...
Sarah K. Tyler, Shenghuo Zhu, Yun Chi, Yi Zhang
FOCS
2008
IEEE
14 years 3 months ago
Mixing Time of Exponential Random Graphs
A plethora of random graph models have been developed in recent years to study a range of problems on networks, driven by the wide availability of data from many social, telecommu...
Shankar Bhamidi, Guy Bresler, Allan Sly