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116
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WSDM
2010
ACM
254views Data Mining» more  WSDM 2010»
16 years 23 days ago
Learning Influence Probabilities In Social Networks
Recently, there has been tremendous interest in the phenomenon of influence propagation in social networks. The studies in this area assume they have as input to their problems a ...
Amit Goyal 0002, Francesco Bonchi, Laks V. S. Laks...
146
Voted
WSDM
2010
ACM
215views Data Mining» more  WSDM 2010»
16 years 23 days ago
GeoFolk: Latent spatial semantics in Web 2.0 social media
We describe an approach for multi-modal characterization of social media by combining text features (e.g. tags as a prominent example of short, unstructured text labels) with spat...
Sergej Sizov
110
Voted
ADMA
2006
Springer
110views Data Mining» more  ADMA 2006»
15 years 7 months ago
Learning with Local Drift Detection
Abstract. Most of the work in Machine Learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem...
João Gama, Gladys Castillo
IS
2007
15 years 3 months ago
Discovering frequent geometric subgraphs
As data mining techniques are being increasingly applied to non-traditional domains, existing approaches for finding frequent itemsets cannot be used as they cannot model the req...
Michihiro Kuramochi, George Karypis
150
Voted
DMIN
2009
136views Data Mining» more  DMIN 2009»
15 years 1 months ago
Evaluating Algorithms for Concept Description
When performing concept description, models need to be evaluated both on accuracy and comprehensibility. A comprehensible concept description model should present the most importan...
Cecilia Sönströd, Ulf Johansson, Tuve L&...