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KDD
2008
ACM
156views Data Mining» more  KDD 2008»
15 years 11 months ago
Can complex network metrics predict the behavior of NBA teams?
The United States National Basketball Association (NBA) is one of the most popular sports league in the world and is well known for moving a millionary betting market that uses th...
Antonio Alfredo Ferreira Loureiro, Pedro O. S. Vaz...
KDD
2008
ACM
138views Data Mining» more  KDD 2008»
15 years 11 months ago
A visual-analytic toolkit for dynamic interaction graphs
In this article we describe a visual-analytic tool for the interrogation of evolving interaction network data such as those found in social, bibliometric, WWW and biological appli...
Xintian Yang, Sitaram Asur, Srinivasan Parthasarat...
KDD
2008
ACM
259views Data Mining» more  KDD 2008»
15 years 11 months ago
Using ghost edges for classification in sparsely labeled networks
We address the problem of classification in partially labeled networks (a.k.a. within-network classification) where observed class labels are sparse. Techniques for statistical re...
Brian Gallagher, Hanghang Tong, Tina Eliassi-Rad, ...
KDD
2008
ACM
164views Data Mining» more  KDD 2008»
15 years 11 months ago
Microscopic evolution of social networks
We present a detailed study of network evolution by analyzing four large online social networks with full temporal information about node and edge arrivals. For the first time at ...
Jure Leskovec, Lars Backstrom, Ravi Kumar, Andrew ...
KDD
2008
ACM
119views Data Mining» more  KDD 2008»
15 years 11 months ago
SAIL: summation-based incremental learning for information-theoretic clustering
Information-theoretic clustering aims to exploit information theoretic measures as the clustering criteria. A common practice on this topic is so-called INFO-K-means, which perfor...
Junjie Wu, Hui Xiong, Jian Chen
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