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KDD
2009
ACM
198views Data Mining» more  KDD 2009»
15 years 10 months ago
Heterogeneous source consensus learning via decision propagation and negotiation
Nowadays, enormous amounts of data are continuously generated not only in massive scale, but also from different, sometimes conflicting, views. Therefore, it is important to conso...
Jing Gao, Wei Fan, Yizhou Sun, Jiawei Han
KDD
2009
ACM
229views Data Mining» more  KDD 2009»
15 years 10 months ago
Relational learning via latent social dimensions
Social media such as blogs, Facebook, Flickr, etc., presents data in a network format rather than classical IID distribution. To address the interdependency among data instances, ...
Lei Tang, Huan Liu
KDD
2009
ACM
179views Data Mining» more  KDD 2009»
15 years 4 months ago
Identifying graphs from noisy and incomplete data
There is a growing wealth of data describing networks of various types, including social networks, physical networks such as transportation or communication networks, and biologic...
Galileo Mark S. Namata Jr., Lise Getoor
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Voted
KDD
2009
ACM
194views Data Mining» more  KDD 2009»
15 years 10 months ago
Combining link and content for community detection: a discriminative approach
In this paper, we consider the problem of combining link and content analysis for community detection from networked data, such as paper citation networks and Word Wide Web. Most ...
Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu
KDD
2009
ACM
152views Data Mining» more  KDD 2009»
15 years 10 months ago
TANGENT: a novel, 'Surprise me', recommendation algorithm
Most of recommender systems try to find items that are most relevant to the older choices of a given user. Here we focus on the "surprise me" query: A user may be bored ...
Kensuke Onuma, Hanghang Tong, Christos Faloutsos