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» Sampling Representative Users from Large Social Networks
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UAI
2003
13 years 8 months ago
Large-Sample Learning of Bayesian Networks is NP-Hard
In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesian networks from data. Our results apply whenever the learning algorithm uses a ...
David Maxwell Chickering, Christopher Meek, David ...
TRIDENTCOM
2010
IEEE
13 years 5 months ago
Characterizing User Behavior and Network Load on a Large-Scale Wireless Mesh Network
Wireless mesh networks represent a promising paradigm to provide a scalable infrastructure for Internet access in metropolitan areas. In this paper, a large-scale wireless mesh tes...
Michele Vincenzi, Roberto Tomasi, David Tacconi, D...
KDD
2009
ACM
164views Data Mining» more  KDD 2009»
14 years 8 months ago
Social influence analysis in large-scale networks
In large social networks, nodes (users, entities) are influenced by others for various reasons. For example, the colleagues have strong influence on one's work, while the fri...
Jie Tang, Jimeng Sun, Chi Wang, Zi Yang
SIGIR
2009
ACM
14 years 2 months ago
On social networks and collaborative recommendation
Social network systems, like last.fm, play a significant role in Web 2.0, containing large amounts of multimedia-enriched data that are enhanced both by explicit user-provided an...
Ioannis Konstas, Vassilios Stathopoulos, Joemon M....
BMCBI
2008
119views more  BMCBI 2008»
13 years 7 months ago
A new method for 2D gel spot alignment: application to the analysis of large sample sets in clinical proteomics
Background: In current comparative proteomics studies, the large number of images generated by 2D gels is currently compared using spot matching algorithms. Unfortunately, differe...
Sabine Pérès, Laurence Molina, Nicol...