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ICDM
2005
IEEE
177views Data Mining» more  ICDM 2005»
13 years 10 months ago
Pruning Social Networks Using Structural Properties and Descriptive Attributes
Scale is often an issue with understanding and making sense of large social networks. Here we investigate methods for pruning social networks by determining the most relevant rela...
Lisa Singh, Lise Getoor, Louis Licamele
WWW
2010
ACM
13 years 5 months ago
Randomization tests for distinguishing social influence and homophily effects
Relational autocorrelation is ubiquitous in relational domains. This observed correlation between class labels of linked instances in a network (e.g., two friends are more likely ...
Timothy La Fond, Jennifer Neville
JMLR
2002
138views more  JMLR 2002»
13 years 4 months ago
Learning Probabilistic Models of Link Structure
Most real-world data is heterogeneous and richly interconnected. Examples include the Web, hypertext, bibliometric data and social networks. In contrast, most statistical learning...
Lise Getoor, Nir Friedman, Daphne Koller, Benjamin...
SDM
2012
SIAM
307views Data Mining» more  SDM 2012»
11 years 7 months ago
Pseudo Cold Start Link Prediction with Multiple Sources in Social Networks
Link prediction is an important task in social networks and data mining for understanding the mechanisms by which the social networks form and evolve. In most link prediction rese...
Liang Ge, Aidong Zhang
CSE
2009
IEEE
13 years 11 months ago
Predicting Interests of People on Online Social Networks
—We introduce a new data set which contains both a self-declared friendship network and self-chosen attributes from a finite list defined by the social networking site. We prop...
Apoorv Agarwal, Owen Rambow, Nandini Bhardwaj